{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['新增及留存', '商品销售情况', '商品浏览情况', '商品信息']\n"
     ]
    },
    {
     "data": {
      "text/plain": "dict"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "excel = pd.ExcelFile('指标作业用数据.xls')\n",
    "sheet_names = excel.sheet_names\n",
    "print(sheet_names)\n",
    "excel_datas = excel.parse(sheet_name=sheet_names)\n",
    "type(excel_datas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1 月 7 日当天的 DAU 是多少？\n",
    "\n",
    "答：1月7日当天的日活跃用户数量是当天新增的用户数量加上前几天留存的用户数量，值为 30420"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['日期', '当日新增', '1日留存', '2日留存', '3日留存', '4日留存', '5日留存', '6日留存', '7日留存'], dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": "(59, 32)"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DAUs = excel_datas['新增及留存']\n",
    "print(DAUs.columns[:9])\n",
    "DAUs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dau(day):\n",
    "    dau = 0\n",
    "    for i in range(1, day+1):\n",
    "        dau += DAUs.iloc[day-i][i]\n",
    "    return dau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1月7日当天DAU是\t30420\n"
     ]
    }
   ],
   "source": [
    "dau_01_07 = get_dau(7)\n",
    "        \n",
    "print(\"1月7日当天DAU是\\t{:.0f}\".format(dau_01_07))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": "           日期   当日新增    1日留存    2日留存    3日留存    4日留存    5日留存    6日留存    7日留存  \\\n0  2018-01-01   8242  3441.0  3033.0  3008.0  2960.0  2853.0  2750.0  2397.0   \n1  2018-01-02   8292  3432.0  3333.0  3587.0  2922.0  2627.0  3199.0  2365.0   \n2  2018-01-03  11015  5144.0  4433.0  4501.0  3797.0  4055.0  4275.0  3704.0   \n3  2018-01-04   9486  4420.0  3717.0  3916.0  3077.0  3395.0  3383.0  3170.0   \n4  2018-01-05   8592  3823.0  3893.0  2969.0  2838.0  3402.0  2835.0  2709.0   \n5  2018-01-06   8853  3673.0  3718.0  3260.0  3264.0  3428.0  2659.0  2968.0   \n6  2018-01-07   9764  3905.0  4590.0  4293.0  3815.0  3688.0  3482.0  3236.0   \n7  2018-01-08   8441  4410.0  3389.0  3403.0  2890.0  2588.0  3023.0  2744.0   \n8  2018-01-09   9143  4114.0  3667.0  3628.0  3715.0  2821.0  2790.0  2620.0   \n9  2018-01-10   8706  3612.0  3339.0  3367.0  3388.0  2984.0  3127.0  2562.0   \n10 2018-01-11   8905  3833.0  4112.0  3904.0  2995.0  2916.0  2751.0  2705.0   \n11 2018-01-12  10060  4416.0  4659.0  3988.0  3625.0  3608.0  3368.0  3256.0   \n12 2018-01-13   9875  4024.0  4519.0  3894.0  3381.0  3407.0  3719.0  2948.0   \n13 2018-01-14   9456  4543.0  3597.0  3693.0  3532.0  3313.0  3267.0  3276.0   \n14 2018-01-15  10542  5091.0  4902.0  3918.0  4339.0  3304.0  4040.0  3309.0   \n15 2018-01-16   9247  4720.0  3453.0  3639.0  3717.0  3538.0  3099.0  3009.0   \n16 2018-01-17   9276  3821.0  4032.0  3338.0  3510.0  3348.0  2814.0  3097.0   \n17 2018-01-18   9450  4710.0  4303.0  3329.0  3798.0  3066.0  2926.0  2986.0   \n18 2018-01-19   8425  4065.0  3728.0  3623.0  3130.0  3214.0  2923.0  2606.0   \n19 2018-01-20  10551  5196.0  4368.0  3939.0  3410.0  3700.0  3431.0  3681.0   \n20 2018-01-21   8312  4293.0  3976.0  2824.0  3171.0  3032.0  2860.0  2696.0   \n21 2018-01-22   8378  4314.0  3391.0  3367.0  3049.0  3199.0  2892.0  2492.0   \n22 2018-01-23  10081  4062.0  3872.0  3882.0  3274.0  3589.0  3427.0  3355.0   \n23 2018-01-24  10296  4272.0  4835.0  3524.0  3896.0  3638.0  3123.0  2918.0   \n24 2018-01-25  10592  5179.0  4955.0  4595.0  3940.0  3896.0  3456.0  3102.0   \n25 2018-01-26  10951  4648.0  4342.0  4088.0  4078.0  3686.0  3533.0  3721.0   \n26 2018-01-27  11272  5382.0  4495.0  4828.0  4459.0  4107.0  3653.0  3759.0   \n27 2018-01-28   9931  3997.0  4285.0  4367.0  3368.0  3690.0  3240.0  3100.0   \n28 2018-01-29   8981  4566.0  4011.0  3613.0  3513.0  2825.0  2957.0  2608.0   \n29 2018-01-30  10740  5219.0  4090.0  4727.0  3496.0  4240.0  4164.0  3138.0   \n30 2018-01-31  10401  5023.0  4114.0  3970.0  3977.0  3209.0  3117.0  3050.0   \n31 2018-02-01   9109  3966.0  3737.0  3167.0  3424.0  2904.0  3228.0  2856.0   \n32 2018-02-02  10205  5332.0  4867.0  3806.0  4216.0  3870.0  3194.0  3071.0   \n33 2018-02-03   7716  3510.0  3261.0  3399.0  2515.0  2829.0  2321.0  2800.0   \n34 2018-02-04  10528  4753.0  4842.0  4156.0  3701.0  3572.0  3809.0  3695.0   \n35 2018-02-05   7223  3264.0  3482.0  2663.0  2360.0  2311.0  2183.0  2608.0   \n36 2018-02-06   7752  3825.0  3598.0  2633.0  2679.0  2975.0  2862.0  2696.0   \n37 2018-02-07   8540  4385.0  3794.0  3081.0  3125.0  2820.0  2786.0  2669.0   \n38 2018-02-08  11206  4947.0  4123.0  4838.0  4589.0  4147.0  4055.0  3612.0   \n39 2018-02-09   9606  4221.0  4577.0  3974.0  3235.0  3715.0  3088.0  3197.0   \n40 2018-02-10   9861  4210.0  4091.0  3715.0  3372.0  3747.0  3291.0  3216.0   \n41 2018-02-11  10465  5358.0  4809.0  4241.0  3759.0  3225.0  3527.0  3739.0   \n42 2018-02-12  11039  4796.0  4285.0  3713.0  4243.0  3565.0  4039.0  3921.0   \n43 2018-02-13   7710  3754.0  2957.0  2901.0  3176.0  2630.0  2778.0  2676.0   \n44 2018-02-14   6630  3275.0  2818.0  2589.0  2652.0  2511.0  2031.0  2283.0   \n45 2018-02-15   5922  2848.0  2743.0  2395.0  1935.0  2088.0  1982.0  2136.0   \n46 2018-02-16   4562  2244.0  1724.0  1919.0  1790.0  1447.0  1608.0  1614.0   \n47 2018-02-17   3879  1561.0  1852.0  1506.0  1295.0  1241.0  1430.0  1387.0   \n48 2018-02-18   3299  1557.0  1267.0  1353.0  1377.0  1308.0  1279.0  1109.0   \n49 2018-02-19   2081   913.0   819.0   809.0   669.0   726.0   779.0   681.0   \n50 2018-02-20   3318  1597.0  1550.0  1166.0  1240.0  1014.0  1168.0  1122.0   \n51 2018-02-21   4293  1779.0  1941.0  1714.0  1720.0  1455.0  1435.0  1235.0   \n52 2018-02-22   6333  3008.0  2948.0  2638.0  2657.0  2180.0  1944.0     NaN   \n53 2018-02-23   7569  3901.0  3481.0  2667.0  2951.0  2812.0     NaN     NaN   \n54 2018-02-24   7296  3243.0  3379.0  3033.0  2897.0     NaN     NaN     NaN   \n55 2018-02-25   8388  3652.0  3970.0  3311.0     NaN     NaN     NaN     NaN   \n56 2018-02-26  10001  4160.0  4388.0     NaN     NaN     NaN     NaN     NaN   \n57 2018-02-27   9094  3710.0     NaN     NaN     NaN     NaN     NaN     NaN   \n58 2018-02-28   9846     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n\n      8日留存  ...   21日留存   22日留存   23日留存   24日留存   25日留存   26日留存   27日留存  \\\n0   2521.0  ...  1469.0  1509.0  1483.0  1633.0  1451.0  1221.0  1391.0   \n1   2673.0  ...  1445.0  1680.0  1401.0  1557.0  1293.0  1140.0  1250.0   \n2   2937.0  ...  2234.0  2403.0  2260.0  2313.0  2013.0  1902.0  1917.0   \n3   3039.0  ...  1950.0  1976.0  1563.0  1587.0  1715.0  1399.0  1420.0   \n4   2480.0  ...  1764.0  1652.0  1659.0  1546.0  1541.0  1368.0  1425.0   \n5   2404.0  ...  1853.0  1526.0  1627.0  1446.0  1513.0  1413.0  1434.0   \n6   3212.0  ...  1952.0  1878.0  1695.0  1634.0  1918.0  1399.0  1372.0   \n7   2799.0  ...  1533.0  1430.0  1630.0  1561.0  1549.0  1191.0  1456.0   \n8   2637.0  ...  2016.0  1969.0  1746.0  1629.0  1815.0  1288.0  1468.0   \n9   2370.0  ...  1786.0  1471.0  1809.0  1760.0  1516.0  1266.0  1388.0   \n10  2659.0  ...  1565.0  1939.0  1444.0  1650.0  1355.0  1544.0  1427.0   \n11  2785.0  ...  1865.0  1990.0  2044.0  2092.0  1926.0  1460.0  1776.0   \n12  2646.0  ...  2148.0  1754.0  1696.0  1736.0  1673.0  1554.0  1489.0   \n13  2930.0  ...  1735.0  1991.0  1637.0  1781.0  1713.0  1398.0  1686.0   \n14  2856.0  ...  2266.0  2289.0  1872.0  2019.0  1738.0  1514.0  1435.0   \n15  2782.0  ...  2002.0  1868.0  1779.0  1596.0  1611.0  1353.0  1633.0   \n16  3106.0  ...  2025.0  1920.0  1823.0  1499.0  1483.0  1528.0  1605.0   \n17  3218.0  ...  1712.0  1698.0  1861.0  1776.0  1797.0  1343.0  1436.0   \n18  2796.0  ...  1694.0  1753.0  1676.0  1496.0  1287.0  1208.0  1294.0   \n19  2973.0  ...  2297.0  1823.0  1755.0  2184.0  1874.0  1519.0  1693.0   \n20  2350.0  ...  1740.0  1754.0  1582.0  1536.0  1590.0  1422.0  1179.0   \n21  2714.0  ...  1833.0  1555.0  1645.0  1692.0  1598.0  1367.0  1160.0   \n22  3469.0  ...  1708.0  2210.0  2084.0  1824.0  1907.0  1727.0  1466.0   \n23  3241.0  ...  2157.0  2082.0  2018.0  1822.0  1574.0  1676.0  1689.0   \n24  3152.0  ...  1970.0  2282.0  2201.0  1838.0  1835.0  1505.0  1694.0   \n25  3657.0  ...  2299.0  2023.0  2279.0  2032.0  1689.0  1872.0  1744.0   \n26  3794.0  ...  2262.0  2149.0  2031.0  1981.0  2077.0  1628.0  1745.0   \n27  3005.0  ...  2037.0  1670.0  1934.0  1686.0  1681.0  1529.0  1673.0   \n28  3046.0  ...  1967.0  1729.0  1837.0  1650.0  1742.0  1497.0  1538.0   \n29  3579.0  ...  1989.0  2009.0  1780.0  2102.0  1828.0  1577.0  1551.0   \n30  3576.0  ...  1891.0  2251.0  1940.0  2138.0  1750.0  1727.0  1814.0   \n31  3042.0  ...  1754.0  2000.0  1628.0  1574.0  1478.0  1477.0  1375.0   \n32  3030.0  ...  2155.0  1780.0  1722.0  1867.0  1584.0  1505.0     NaN   \n33  2401.0  ...  1362.0  1440.0  1345.0  1609.0  1463.0     NaN     NaN   \n34  3078.0  ...  1894.0  1815.0  1876.0  2185.0     NaN     NaN     NaN   \n35  2471.0  ...  1333.0  1500.0  1240.0     NaN     NaN     NaN     NaN   \n36  2558.0  ...  1308.0  1644.0     NaN     NaN     NaN     NaN     NaN   \n37  2409.0  ...  1461.0     NaN     NaN     NaN     NaN     NaN     NaN   \n38  3653.0  ...     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n39  2719.0  ...     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n40  2948.0  ...     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n41  3066.0  ...     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n42  3784.0  ...     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n43  2373.0  ...     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n44  2231.0  ...     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n45  1567.0  ...     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n46  1303.0  ...     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n47  1245.0  ...     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n48  1115.0  ...     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n49   605.0  ...     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n50  1039.0  ...     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n51     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n52     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n53     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n54     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n55     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n56     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n57     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n58     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n\n     28日留存   29日留存   30日留存  \n0   1090.0  1081.0  1254.0  \n1   1221.0  1083.0  1095.0  \n2   1531.0  1695.0  1676.0  \n3   1397.0  1331.0  1466.0  \n4   1209.0  1190.0  1297.0  \n5   1147.0  1487.0  1181.0  \n6   1271.0  1634.0  1496.0  \n7   1237.0  1382.0  1145.0  \n8   1299.0  1258.0  1326.0  \n9   1349.0  1335.0  1388.0  \n10  1473.0  1203.0  1175.0  \n11  1323.0  1648.0  1366.0  \n12  1297.0  1437.0  1355.0  \n13  1249.0  1525.0  1526.0  \n14  1506.0  1612.0  1497.0  \n15  1392.0  1480.0  1288.0  \n16  1230.0  1248.0  1343.0  \n17  1431.0  1381.0  1332.0  \n18  1283.0  1411.0  1217.0  \n19  1591.0  1598.0  1384.0  \n20  1208.0  1360.0  1098.0  \n21  1144.0  1182.0  1193.0  \n22  1562.0  1683.0  1379.0  \n23  1681.0  1675.0  1607.0  \n24  1364.0  1372.0  1508.0  \n25  1526.0  1484.0  1715.0  \n26  1563.0  1560.0  1527.0  \n27  1352.0  1472.0  1356.0  \n28  1301.0  1349.0  1482.0  \n29  1539.0  1678.0     NaN  \n30  1504.0     NaN     NaN  \n31     NaN     NaN     NaN  \n32     NaN     NaN     NaN  \n33     NaN     NaN     NaN  \n34     NaN     NaN     NaN  \n35     NaN     NaN     NaN  \n36     NaN     NaN     NaN  \n37     NaN     NaN     NaN  \n38     NaN     NaN     NaN  \n39     NaN     NaN     NaN  \n40     NaN     NaN     NaN  \n41     NaN     NaN     NaN  \n42     NaN     NaN     NaN  \n43     NaN     NaN     NaN  \n44     NaN     NaN     NaN  \n45     NaN     NaN     NaN  \n46     NaN     NaN     NaN  \n47     NaN     NaN     NaN  \n48     NaN     NaN     NaN  \n49     NaN     NaN     NaN  \n50     NaN     NaN     NaN  \n51     NaN     NaN     NaN  \n52     NaN     NaN     NaN  \n53     NaN     NaN     NaN  \n54     NaN     NaN     NaN  \n55     NaN     NaN     NaN  \n56     NaN     NaN     NaN  \n57     NaN     NaN     NaN  \n58     NaN     NaN     NaN  \n\n[59 rows x 32 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>日期</th>\n      <th>当日新增</th>\n      <th>1日留存</th>\n      <th>2日留存</th>\n      <th>3日留存</th>\n      <th>4日留存</th>\n      <th>5日留存</th>\n      <th>6日留存</th>\n      <th>7日留存</th>\n      <th>8日留存</th>\n      <th>...</th>\n      <th>21日留存</th>\n      <th>22日留存</th>\n      <th>23日留存</th>\n      <th>24日留存</th>\n      <th>25日留存</th>\n      <th>26日留存</th>\n      <th>27日留存</th>\n      <th>28日留存</th>\n      <th>29日留存</th>\n      <th>30日留存</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2018-01-01</td>\n      <td>8242</td>\n      <td>3441.0</td>\n      <td>3033.0</td>\n      <td>3008.0</td>\n      <td>2960.0</td>\n      <td>2853.0</td>\n      <td>2750.0</td>\n      <td>2397.0</td>\n      <td>2521.0</td>\n      <td>...</td>\n      <td>1469.0</td>\n      <td>1509.0</td>\n      <td>1483.0</td>\n      <td>1633.0</td>\n      <td>1451.0</td>\n      <td>1221.0</td>\n      <td>1391.0</td>\n      <td>1090.0</td>\n      <td>1081.0</td>\n      <td>1254.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2018-01-02</td>\n      <td>8292</td>\n      <td>3432.0</td>\n      <td>3333.0</td>\n      <td>3587.0</td>\n      <td>2922.0</td>\n      <td>2627.0</td>\n      <td>3199.0</td>\n      <td>2365.0</td>\n      <td>2673.0</td>\n      <td>...</td>\n      <td>1445.0</td>\n      <td>1680.0</td>\n      <td>1401.0</td>\n      <td>1557.0</td>\n      <td>1293.0</td>\n      <td>1140.0</td>\n      <td>1250.0</td>\n      <td>1221.0</td>\n      <td>1083.0</td>\n      <td>1095.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2018-01-03</td>\n      <td>11015</td>\n      <td>5144.0</td>\n      <td>4433.0</td>\n      <td>4501.0</td>\n      <td>3797.0</td>\n      <td>4055.0</td>\n      <td>4275.0</td>\n      <td>3704.0</td>\n      <td>2937.0</td>\n      <td>...</td>\n      <td>2234.0</td>\n      <td>2403.0</td>\n      <td>2260.0</td>\n      <td>2313.0</td>\n      <td>2013.0</td>\n      <td>1902.0</td>\n      <td>1917.0</td>\n      <td>1531.0</td>\n      <td>1695.0</td>\n      <td>1676.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2018-01-04</td>\n      <td>9486</td>\n      <td>4420.0</td>\n      <td>3717.0</td>\n      <td>3916.0</td>\n      <td>3077.0</td>\n      <td>3395.0</td>\n      <td>3383.0</td>\n      <td>3170.0</td>\n      <td>3039.0</td>\n      <td>...</td>\n      <td>1950.0</td>\n      <td>1976.0</td>\n      <td>1563.0</td>\n      <td>1587.0</td>\n      <td>1715.0</td>\n      <td>1399.0</td>\n      <td>1420.0</td>\n      <td>1397.0</td>\n      <td>1331.0</td>\n      <td>1466.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2018-01-05</td>\n      <td>8592</td>\n      <td>3823.0</td>\n      <td>3893.0</td>\n      <td>2969.0</td>\n      <td>2838.0</td>\n      <td>3402.0</td>\n      <td>2835.0</td>\n      <td>2709.0</td>\n      <td>2480.0</td>\n      <td>...</td>\n      <td>1764.0</td>\n      <td>1652.0</td>\n      <td>1659.0</td>\n      <td>1546.0</td>\n      <td>1541.0</td>\n      <td>1368.0</td>\n      <td>1425.0</td>\n      <td>1209.0</td>\n      <td>1190.0</td>\n      <td>1297.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2018-01-06</td>\n      <td>8853</td>\n      <td>3673.0</td>\n      <td>3718.0</td>\n      <td>3260.0</td>\n      <td>3264.0</td>\n      <td>3428.0</td>\n      <td>2659.0</td>\n      <td>2968.0</td>\n      <td>2404.0</td>\n      <td>...</td>\n      <td>1853.0</td>\n      <td>1526.0</td>\n      <td>1627.0</td>\n      <td>1446.0</td>\n      <td>1513.0</td>\n      <td>1413.0</td>\n      <td>1434.0</td>\n      <td>1147.0</td>\n      <td>1487.0</td>\n      <td>1181.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2018-01-07</td>\n      <td>9764</td>\n      <td>3905.0</td>\n      <td>4590.0</td>\n      <td>4293.0</td>\n      <td>3815.0</td>\n      <td>3688.0</td>\n      <td>3482.0</td>\n      <td>3236.0</td>\n      <td>3212.0</td>\n      <td>...</td>\n      <td>1952.0</td>\n      <td>1878.0</td>\n      <td>1695.0</td>\n      <td>1634.0</td>\n      <td>1918.0</td>\n      <td>1399.0</td>\n      <td>1372.0</td>\n      <td>1271.0</td>\n      <td>1634.0</td>\n      <td>1496.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2018-01-08</td>\n      <td>8441</td>\n      <td>4410.0</td>\n      <td>3389.0</td>\n      <td>3403.0</td>\n      <td>2890.0</td>\n      <td>2588.0</td>\n      <td>3023.0</td>\n      <td>2744.0</td>\n      <td>2799.0</td>\n      <td>...</td>\n      <td>1533.0</td>\n      <td>1430.0</td>\n      <td>1630.0</td>\n      <td>1561.0</td>\n      <td>1549.0</td>\n      <td>1191.0</td>\n      <td>1456.0</td>\n      <td>1237.0</td>\n      <td>1382.0</td>\n      <td>1145.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>2018-01-09</td>\n      <td>9143</td>\n      <td>4114.0</td>\n      <td>3667.0</td>\n      <td>3628.0</td>\n      <td>3715.0</td>\n      <td>2821.0</td>\n      <td>2790.0</td>\n      <td>2620.0</td>\n      <td>2637.0</td>\n      <td>...</td>\n      <td>2016.0</td>\n      <td>1969.0</td>\n      <td>1746.0</td>\n      <td>1629.0</td>\n      <td>1815.0</td>\n      <td>1288.0</td>\n      <td>1468.0</td>\n      <td>1299.0</td>\n      <td>1258.0</td>\n      <td>1326.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2018-01-10</td>\n      <td>8706</td>\n      <td>3612.0</td>\n      <td>3339.0</td>\n      <td>3367.0</td>\n      <td>3388.0</td>\n      <td>2984.0</td>\n      <td>3127.0</td>\n      <td>2562.0</td>\n      <td>2370.0</td>\n      <td>...</td>\n      <td>1786.0</td>\n      <td>1471.0</td>\n      <td>1809.0</td>\n      <td>1760.0</td>\n      <td>1516.0</td>\n      <td>1266.0</td>\n      <td>1388.0</td>\n      <td>1349.0</td>\n      <td>1335.0</td>\n      <td>1388.0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>2018-01-11</td>\n      <td>8905</td>\n      <td>3833.0</td>\n      <td>4112.0</td>\n      <td>3904.0</td>\n      <td>2995.0</td>\n      <td>2916.0</td>\n      <td>2751.0</td>\n      <td>2705.0</td>\n      <td>2659.0</td>\n      <td>...</td>\n      <td>1565.0</td>\n      <td>1939.0</td>\n      <td>1444.0</td>\n      <td>1650.0</td>\n      <td>1355.0</td>\n      <td>1544.0</td>\n      <td>1427.0</td>\n      <td>1473.0</td>\n      <td>1203.0</td>\n      <td>1175.0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>2018-01-12</td>\n      <td>10060</td>\n      <td>4416.0</td>\n      <td>4659.0</td>\n      <td>3988.0</td>\n      <td>3625.0</td>\n      <td>3608.0</td>\n      <td>3368.0</td>\n      <td>3256.0</td>\n      <td>2785.0</td>\n      <td>...</td>\n      <td>1865.0</td>\n      <td>1990.0</td>\n      <td>2044.0</td>\n      <td>2092.0</td>\n      <td>1926.0</td>\n      <td>1460.0</td>\n      <td>1776.0</td>\n      <td>1323.0</td>\n      <td>1648.0</td>\n      <td>1366.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>2018-01-13</td>\n      <td>9875</td>\n      <td>4024.0</td>\n      <td>4519.0</td>\n      <td>3894.0</td>\n      <td>3381.0</td>\n      <td>3407.0</td>\n      <td>3719.0</td>\n      <td>2948.0</td>\n      <td>2646.0</td>\n      <td>...</td>\n      <td>2148.0</td>\n      <td>1754.0</td>\n      <td>1696.0</td>\n      <td>1736.0</td>\n      <td>1673.0</td>\n      <td>1554.0</td>\n      <td>1489.0</td>\n      <td>1297.0</td>\n      <td>1437.0</td>\n      <td>1355.0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>2018-01-14</td>\n      <td>9456</td>\n      <td>4543.0</td>\n      <td>3597.0</td>\n      <td>3693.0</td>\n      <td>3532.0</td>\n      <td>3313.0</td>\n      <td>3267.0</td>\n      <td>3276.0</td>\n      <td>2930.0</td>\n      <td>...</td>\n      <td>1735.0</td>\n      <td>1991.0</td>\n      <td>1637.0</td>\n      <td>1781.0</td>\n      <td>1713.0</td>\n      <td>1398.0</td>\n      <td>1686.0</td>\n      <td>1249.0</td>\n      <td>1525.0</td>\n      <td>1526.0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>2018-01-15</td>\n      <td>10542</td>\n      <td>5091.0</td>\n      <td>4902.0</td>\n      <td>3918.0</td>\n      <td>4339.0</td>\n      <td>3304.0</td>\n      <td>4040.0</td>\n      <td>3309.0</td>\n      <td>2856.0</td>\n      <td>...</td>\n      <td>2266.0</td>\n      <td>2289.0</td>\n      <td>1872.0</td>\n      <td>2019.0</td>\n      <td>1738.0</td>\n      <td>1514.0</td>\n      <td>1435.0</td>\n      <td>1506.0</td>\n      <td>1612.0</td>\n      <td>1497.0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>2018-01-16</td>\n      <td>9247</td>\n      <td>4720.0</td>\n      <td>3453.0</td>\n      <td>3639.0</td>\n      <td>3717.0</td>\n      <td>3538.0</td>\n      <td>3099.0</td>\n      <td>3009.0</td>\n      <td>2782.0</td>\n      <td>...</td>\n      <td>2002.0</td>\n      <td>1868.0</td>\n      <td>1779.0</td>\n      <td>1596.0</td>\n      <td>1611.0</td>\n      <td>1353.0</td>\n      <td>1633.0</td>\n      <td>1392.0</td>\n      <td>1480.0</td>\n      <td>1288.0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>2018-01-17</td>\n      <td>9276</td>\n      <td>3821.0</td>\n      <td>4032.0</td>\n      <td>3338.0</td>\n      <td>3510.0</td>\n      <td>3348.0</td>\n      <td>2814.0</td>\n      <td>3097.0</td>\n      <td>3106.0</td>\n      <td>...</td>\n      <td>2025.0</td>\n      <td>1920.0</td>\n      <td>1823.0</td>\n      <td>1499.0</td>\n      <td>1483.0</td>\n      <td>1528.0</td>\n      <td>1605.0</td>\n      <td>1230.0</td>\n      <td>1248.0</td>\n      <td>1343.0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>2018-01-18</td>\n      <td>9450</td>\n      <td>4710.0</td>\n      <td>4303.0</td>\n      <td>3329.0</td>\n      <td>3798.0</td>\n      <td>3066.0</td>\n      <td>2926.0</td>\n      <td>2986.0</td>\n      <td>3218.0</td>\n      <td>...</td>\n      <td>1712.0</td>\n      <td>1698.0</td>\n      <td>1861.0</td>\n      <td>1776.0</td>\n      <td>1797.0</td>\n      <td>1343.0</td>\n      <td>1436.0</td>\n      <td>1431.0</td>\n      <td>1381.0</td>\n      <td>1332.0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>2018-01-19</td>\n      <td>8425</td>\n      <td>4065.0</td>\n      <td>3728.0</td>\n      <td>3623.0</td>\n      <td>3130.0</td>\n      <td>3214.0</td>\n      <td>2923.0</td>\n      <td>2606.0</td>\n      <td>2796.0</td>\n      <td>...</td>\n      <td>1694.0</td>\n      <td>1753.0</td>\n      <td>1676.0</td>\n      <td>1496.0</td>\n      <td>1287.0</td>\n      <td>1208.0</td>\n      <td>1294.0</td>\n      <td>1283.0</td>\n      <td>1411.0</td>\n      <td>1217.0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>2018-01-20</td>\n      <td>10551</td>\n      <td>5196.0</td>\n      <td>4368.0</td>\n      <td>3939.0</td>\n      <td>3410.0</td>\n      <td>3700.0</td>\n      <td>3431.0</td>\n      <td>3681.0</td>\n      <td>2973.0</td>\n      <td>...</td>\n      <td>2297.0</td>\n      <td>1823.0</td>\n      <td>1755.0</td>\n      <td>2184.0</td>\n      <td>1874.0</td>\n      <td>1519.0</td>\n      <td>1693.0</td>\n      <td>1591.0</td>\n      <td>1598.0</td>\n      <td>1384.0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>2018-01-21</td>\n      <td>8312</td>\n      <td>4293.0</td>\n      <td>3976.0</td>\n      <td>2824.0</td>\n      <td>3171.0</td>\n      <td>3032.0</td>\n      <td>2860.0</td>\n      <td>2696.0</td>\n      <td>2350.0</td>\n      <td>...</td>\n      <td>1740.0</td>\n      <td>1754.0</td>\n      <td>1582.0</td>\n      <td>1536.0</td>\n      <td>1590.0</td>\n      <td>1422.0</td>\n      <td>1179.0</td>\n      <td>1208.0</td>\n      <td>1360.0</td>\n      <td>1098.0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>2018-01-22</td>\n      <td>8378</td>\n      <td>4314.0</td>\n      <td>3391.0</td>\n      <td>3367.0</td>\n      <td>3049.0</td>\n      <td>3199.0</td>\n      <td>2892.0</td>\n      <td>2492.0</td>\n      <td>2714.0</td>\n      <td>...</td>\n      <td>1833.0</td>\n      <td>1555.0</td>\n      <td>1645.0</td>\n      <td>1692.0</td>\n      <td>1598.0</td>\n      <td>1367.0</td>\n      <td>1160.0</td>\n      <td>1144.0</td>\n      <td>1182.0</td>\n      <td>1193.0</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>2018-01-23</td>\n      <td>10081</td>\n      <td>4062.0</td>\n      <td>3872.0</td>\n      <td>3882.0</td>\n      <td>3274.0</td>\n      <td>3589.0</td>\n      <td>3427.0</td>\n      <td>3355.0</td>\n      <td>3469.0</td>\n      <td>...</td>\n      <td>1708.0</td>\n      <td>2210.0</td>\n      <td>2084.0</td>\n      <td>1824.0</td>\n      <td>1907.0</td>\n      <td>1727.0</td>\n      <td>1466.0</td>\n      <td>1562.0</td>\n      <td>1683.0</td>\n      <td>1379.0</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>2018-01-24</td>\n      <td>10296</td>\n      <td>4272.0</td>\n      <td>4835.0</td>\n      <td>3524.0</td>\n      <td>3896.0</td>\n      <td>3638.0</td>\n      <td>3123.0</td>\n      <td>2918.0</td>\n      <td>3241.0</td>\n      <td>...</td>\n      <td>2157.0</td>\n      <td>2082.0</td>\n      <td>2018.0</td>\n      <td>1822.0</td>\n      <td>1574.0</td>\n      <td>1676.0</td>\n      <td>1689.0</td>\n      <td>1681.0</td>\n      <td>1675.0</td>\n      <td>1607.0</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>2018-01-25</td>\n      <td>10592</td>\n      <td>5179.0</td>\n      <td>4955.0</td>\n      <td>4595.0</td>\n      <td>3940.0</td>\n      <td>3896.0</td>\n      <td>3456.0</td>\n      <td>3102.0</td>\n      <td>3152.0</td>\n      <td>...</td>\n      <td>1970.0</td>\n      <td>2282.0</td>\n      <td>2201.0</td>\n      <td>1838.0</td>\n      <td>1835.0</td>\n      <td>1505.0</td>\n      <td>1694.0</td>\n      <td>1364.0</td>\n      <td>1372.0</td>\n      <td>1508.0</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>2018-01-26</td>\n      <td>10951</td>\n      <td>4648.0</td>\n      <td>4342.0</td>\n      <td>4088.0</td>\n      <td>4078.0</td>\n      <td>3686.0</td>\n      <td>3533.0</td>\n      <td>3721.0</td>\n      <td>3657.0</td>\n      <td>...</td>\n      <td>2299.0</td>\n      <td>2023.0</td>\n      <td>2279.0</td>\n      <td>2032.0</td>\n      <td>1689.0</td>\n      <td>1872.0</td>\n      <td>1744.0</td>\n      <td>1526.0</td>\n      <td>1484.0</td>\n      <td>1715.0</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>2018-01-27</td>\n      <td>11272</td>\n      <td>5382.0</td>\n      <td>4495.0</td>\n      <td>4828.0</td>\n      <td>4459.0</td>\n      <td>4107.0</td>\n      <td>3653.0</td>\n      <td>3759.0</td>\n      <td>3794.0</td>\n      <td>...</td>\n      <td>2262.0</td>\n      <td>2149.0</td>\n      <td>2031.0</td>\n      <td>1981.0</td>\n      <td>2077.0</td>\n      <td>1628.0</td>\n      <td>1745.0</td>\n      <td>1563.0</td>\n      <td>1560.0</td>\n      <td>1527.0</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>2018-01-28</td>\n      <td>9931</td>\n      <td>3997.0</td>\n      <td>4285.0</td>\n      <td>4367.0</td>\n      <td>3368.0</td>\n      <td>3690.0</td>\n      <td>3240.0</td>\n      <td>3100.0</td>\n      <td>3005.0</td>\n      <td>...</td>\n      <td>2037.0</td>\n      <td>1670.0</td>\n      <td>1934.0</td>\n      <td>1686.0</td>\n      <td>1681.0</td>\n      <td>1529.0</td>\n      <td>1673.0</td>\n      <td>1352.0</td>\n      <td>1472.0</td>\n      <td>1356.0</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>2018-01-29</td>\n      <td>8981</td>\n      <td>4566.0</td>\n      <td>4011.0</td>\n      <td>3613.0</td>\n      <td>3513.0</td>\n      <td>2825.0</td>\n      <td>2957.0</td>\n      <td>2608.0</td>\n      <td>3046.0</td>\n      <td>...</td>\n      <td>1967.0</td>\n      <td>1729.0</td>\n      <td>1837.0</td>\n      <td>1650.0</td>\n      <td>1742.0</td>\n      <td>1497.0</td>\n      <td>1538.0</td>\n      <td>1301.0</td>\n      <td>1349.0</td>\n      <td>1482.0</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>2018-01-30</td>\n      <td>10740</td>\n      <td>5219.0</td>\n      <td>4090.0</td>\n      <td>4727.0</td>\n      <td>3496.0</td>\n      <td>4240.0</td>\n      <td>4164.0</td>\n      <td>3138.0</td>\n      <td>3579.0</td>\n      <td>...</td>\n      <td>1989.0</td>\n      <td>2009.0</td>\n      <td>1780.0</td>\n      <td>2102.0</td>\n      <td>1828.0</td>\n      <td>1577.0</td>\n      <td>1551.0</td>\n      <td>1539.0</td>\n      <td>1678.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>2018-01-31</td>\n      <td>10401</td>\n      <td>5023.0</td>\n      <td>4114.0</td>\n      <td>3970.0</td>\n      <td>3977.0</td>\n      <td>3209.0</td>\n      <td>3117.0</td>\n      <td>3050.0</td>\n      <td>3576.0</td>\n      <td>...</td>\n      <td>1891.0</td>\n      <td>2251.0</td>\n      <td>1940.0</td>\n      <td>2138.0</td>\n      <td>1750.0</td>\n      <td>1727.0</td>\n      <td>1814.0</td>\n      <td>1504.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>2018-02-01</td>\n      <td>9109</td>\n      <td>3966.0</td>\n      <td>3737.0</td>\n      <td>3167.0</td>\n      <td>3424.0</td>\n      <td>2904.0</td>\n      <td>3228.0</td>\n      <td>2856.0</td>\n      <td>3042.0</td>\n      <td>...</td>\n      <td>1754.0</td>\n      <td>2000.0</td>\n      <td>1628.0</td>\n      <td>1574.0</td>\n      <td>1478.0</td>\n      <td>1477.0</td>\n      <td>1375.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>2018-02-02</td>\n      <td>10205</td>\n      <td>5332.0</td>\n      <td>4867.0</td>\n      <td>3806.0</td>\n      <td>4216.0</td>\n      <td>3870.0</td>\n      <td>3194.0</td>\n      <td>3071.0</td>\n      <td>3030.0</td>\n      <td>...</td>\n      <td>2155.0</td>\n      <td>1780.0</td>\n      <td>1722.0</td>\n      <td>1867.0</td>\n      <td>1584.0</td>\n      <td>1505.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>2018-02-03</td>\n      <td>7716</td>\n      <td>3510.0</td>\n      <td>3261.0</td>\n      <td>3399.0</td>\n      <td>2515.0</td>\n      <td>2829.0</td>\n      <td>2321.0</td>\n      <td>2800.0</td>\n      <td>2401.0</td>\n      <td>...</td>\n      <td>1362.0</td>\n      <td>1440.0</td>\n      <td>1345.0</td>\n      <td>1609.0</td>\n      <td>1463.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>2018-02-04</td>\n      <td>10528</td>\n      <td>4753.0</td>\n      <td>4842.0</td>\n      <td>4156.0</td>\n      <td>3701.0</td>\n      <td>3572.0</td>\n      <td>3809.0</td>\n      <td>3695.0</td>\n      <td>3078.0</td>\n      <td>...</td>\n      <td>1894.0</td>\n      <td>1815.0</td>\n      <td>1876.0</td>\n      <td>2185.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>2018-02-05</td>\n      <td>7223</td>\n      <td>3264.0</td>\n      <td>3482.0</td>\n      <td>2663.0</td>\n      <td>2360.0</td>\n      <td>2311.0</td>\n      <td>2183.0</td>\n      <td>2608.0</td>\n      <td>2471.0</td>\n      <td>...</td>\n      <td>1333.0</td>\n      <td>1500.0</td>\n      <td>1240.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>2018-02-06</td>\n      <td>7752</td>\n      <td>3825.0</td>\n      <td>3598.0</td>\n      <td>2633.0</td>\n      <td>2679.0</td>\n      <td>2975.0</td>\n      <td>2862.0</td>\n      <td>2696.0</td>\n      <td>2558.0</td>\n      <td>...</td>\n      <td>1308.0</td>\n      <td>1644.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>2018-02-07</td>\n      <td>8540</td>\n      <td>4385.0</td>\n      <td>3794.0</td>\n      <td>3081.0</td>\n      <td>3125.0</td>\n      <td>2820.0</td>\n      <td>2786.0</td>\n      <td>2669.0</td>\n      <td>2409.0</td>\n      <td>...</td>\n      <td>1461.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>2018-02-08</td>\n      <td>11206</td>\n      <td>4947.0</td>\n      <td>4123.0</td>\n      <td>4838.0</td>\n      <td>4589.0</td>\n      <td>4147.0</td>\n      <td>4055.0</td>\n      <td>3612.0</td>\n      <td>3653.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>2018-02-09</td>\n      <td>9606</td>\n      <td>4221.0</td>\n      <td>4577.0</td>\n      <td>3974.0</td>\n      <td>3235.0</td>\n      <td>3715.0</td>\n      <td>3088.0</td>\n      <td>3197.0</td>\n      <td>2719.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>2018-02-10</td>\n      <td>9861</td>\n      <td>4210.0</td>\n      <td>4091.0</td>\n      <td>3715.0</td>\n      <td>3372.0</td>\n      <td>3747.0</td>\n      <td>3291.0</td>\n      <td>3216.0</td>\n      <td>2948.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>2018-02-11</td>\n      <td>10465</td>\n      <td>5358.0</td>\n      <td>4809.0</td>\n      <td>4241.0</td>\n      <td>3759.0</td>\n      <td>3225.0</td>\n      <td>3527.0</td>\n      <td>3739.0</td>\n      <td>3066.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>2018-02-12</td>\n      <td>11039</td>\n      <td>4796.0</td>\n      <td>4285.0</td>\n      <td>3713.0</td>\n      <td>4243.0</td>\n      <td>3565.0</td>\n      <td>4039.0</td>\n      <td>3921.0</td>\n      <td>3784.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>2018-02-13</td>\n      <td>7710</td>\n      <td>3754.0</td>\n      <td>2957.0</td>\n      <td>2901.0</td>\n      <td>3176.0</td>\n      <td>2630.0</td>\n      <td>2778.0</td>\n      <td>2676.0</td>\n      <td>2373.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>2018-02-14</td>\n      <td>6630</td>\n      <td>3275.0</td>\n      <td>2818.0</td>\n      <td>2589.0</td>\n      <td>2652.0</td>\n      <td>2511.0</td>\n      <td>2031.0</td>\n      <td>2283.0</td>\n      <td>2231.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>2018-02-15</td>\n      <td>5922</td>\n      <td>2848.0</td>\n      <td>2743.0</td>\n      <td>2395.0</td>\n      <td>1935.0</td>\n      <td>2088.0</td>\n      <td>1982.0</td>\n      <td>2136.0</td>\n      <td>1567.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>2018-02-16</td>\n      <td>4562</td>\n      <td>2244.0</td>\n      <td>1724.0</td>\n      <td>1919.0</td>\n      <td>1790.0</td>\n      <td>1447.0</td>\n      <td>1608.0</td>\n      <td>1614.0</td>\n      <td>1303.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>2018-02-17</td>\n      <td>3879</td>\n      <td>1561.0</td>\n      <td>1852.0</td>\n      <td>1506.0</td>\n      <td>1295.0</td>\n      <td>1241.0</td>\n      <td>1430.0</td>\n      <td>1387.0</td>\n      <td>1245.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>2018-02-18</td>\n      <td>3299</td>\n      <td>1557.0</td>\n      <td>1267.0</td>\n      <td>1353.0</td>\n      <td>1377.0</td>\n      <td>1308.0</td>\n      <td>1279.0</td>\n      <td>1109.0</td>\n      <td>1115.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>2018-02-19</td>\n      <td>2081</td>\n      <td>913.0</td>\n      <td>819.0</td>\n      <td>809.0</td>\n      <td>669.0</td>\n      <td>726.0</td>\n      <td>779.0</td>\n      <td>681.0</td>\n      <td>605.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>2018-02-20</td>\n      <td>3318</td>\n      <td>1597.0</td>\n      <td>1550.0</td>\n      <td>1166.0</td>\n      <td>1240.0</td>\n      <td>1014.0</td>\n      <td>1168.0</td>\n      <td>1122.0</td>\n      <td>1039.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>2018-02-21</td>\n      <td>4293</td>\n      <td>1779.0</td>\n      <td>1941.0</td>\n      <td>1714.0</td>\n      <td>1720.0</td>\n      <td>1455.0</td>\n      <td>1435.0</td>\n      <td>1235.0</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>2018-02-22</td>\n      <td>6333</td>\n      <td>3008.0</td>\n      <td>2948.0</td>\n      <td>2638.0</td>\n      <td>2657.0</td>\n      <td>2180.0</td>\n      <td>1944.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>2018-02-23</td>\n      <td>7569</td>\n      <td>3901.0</td>\n      <td>3481.0</td>\n      <td>2667.0</td>\n      <td>2951.0</td>\n      <td>2812.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>2018-02-24</td>\n      <td>7296</td>\n      <td>3243.0</td>\n      <td>3379.0</td>\n      <td>3033.0</td>\n      <td>2897.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>2018-02-25</td>\n      <td>8388</td>\n      <td>3652.0</td>\n      <td>3970.0</td>\n      <td>3311.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>2018-02-26</td>\n      <td>10001</td>\n      <td>4160.0</td>\n      <td>4388.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>2018-02-27</td>\n      <td>9094</td>\n      <td>3710.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>2018-02-28</td>\n      <td>9846</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>59 rows × 32 columns</p>\n</div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DAUs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 老板希望得知：「从留存角度来看，质量最高的新增用户来自哪一天？」。（质量高低并无绝对对错，回答时理由充分、自圆其说即可。）\n",
    "\n",
    "答：从留存的角度来看，质量最高的用户来自2月3号。其7日留存率是36.29%，在这两个月中最高。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_re_rate(days=7, head=5):\n",
    "    \"\"\"\n",
    "    days 表示天数，当days=7时，计算7日留存\n",
    "    head 表示展示的行数\n",
    "    \"\"\"\n",
    "    re_rates = DAUs['{}日留存'.format(days)] / DAUs['当日新增']\n",
    "    datas = list(zip(DAUs['日期'],re_rates))\n",
    "    datas.sort(key=lambda x:x[1], reverse=True)\n",
    "\n",
    "    print(\"按照{}日留存率降序：\".format(days))\n",
    "    for i in range(head):\n",
    "        date = datas[i][0].strftime(\"%m月%d日\")\n",
    "        print(\"{} {:.2%}\".format(date, datas[i][1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "按照1日留存率降序：\n",
      "02月02日 52.25%\n",
      "01月08日 52.24%\n",
      "01月21日 51.65%\n",
      "02月23日 51.54%\n",
      "01月22日 51.49%\n"
     ]
    }
   ],
   "source": [
    "show_re_rate(1,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "按照7日留存率降序：\n",
      "02月03日 36.29%\n",
      "02月05日 36.11%\n",
      "02月15日 36.07%\n",
      "02月17日 35.76%\n",
      "02月11日 35.73%\n"
     ]
    }
   ],
   "source": [
    "show_re_rate(7,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SKU 数量是多少？在 2 月 5 日当天，SKU 销售激活率是多少？\n",
    "\n",
    "答：SKU数量是504，在02月05日当天，SKU销售激活率是73.81%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "(504, 60)"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sell = excel_datas['商品销售情况']\n",
    "Sell.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "商品SKU=504\n"
     ]
    }
   ],
   "source": [
    "goods_num = Sell.shape[0]\n",
    "print(\"商品SKU={}\".format(goods_num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "在02月05日当天，SKU销售激活率是73.81%\n"
     ]
    }
   ],
   "source": [
    "goods_sell_num = sum(map(lambda x: x>0,Sell[Sell.columns[36]]))\n",
    "SKU_02_05 = goods_sell_num / goods_num\n",
    "date = Sell.columns[36].strftime(\"%m月%d日\")\n",
    "print(\"在{}当天，SKU销售激活率是{:.2%}\".format(date, SKU_02_05))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 定义 $详情页购买转化率 = \\dfrac{当日售卖件数}{当日页面浏览次数}$，用于衡量某一（某些）商品在当天的售卖情况。请问三星充电器商品详情页购买转化率哪天最高？\n",
    "\n",
    "答：1月8、14、23、28日，2月2、11、12日的转化率最高，达到了100%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "(504, 60)"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Visit = excel_datas['商品浏览情况']\n",
    "Visit.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sell_rate(goods_index, only_rate=False):\n",
    "    \"\"\"根据商品index，计算商品每天的购买转换率\"\"\"\n",
    "    goods_name = Sell.iloc[goods_index][0]\n",
    "    goods_sell_nums = Sell.iloc[goods_index][1:]\n",
    "    goods_visit_nums = Visit.iloc[goods_index][1:]\n",
    "    goods_sell_rate = goods_sell_nums / goods_visit_nums\n",
    "    goods_sell_rate = goods_sell_rate.fillna(value=1)  # 替换 nan\n",
    "    goods_sell_rate_date = list(zip(goods_sell_rate.index, goods_sell_rate))\n",
    "    if only_rate:\n",
    "        return goods_name, list(goods_sell_rate)\n",
    "    return goods_name, goods_sell_rate_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "01月08日详情页购买转化率：100.00%\n",
      "01月14日详情页购买转化率：100.00%\n",
      "01月23日详情页购买转化率：100.00%\n",
      "01月28日详情页购买转化率：100.00%\n",
      "02月02日详情页购买转化率：100.00%\n",
      "02月11日详情页购买转化率：100.00%\n",
      "02月12日详情页购买转化率：100.00%\n",
      "01月01日详情页购买转化率：84.62%\n",
      "01月07日详情页购买转化率：82.50%\n",
      "02月18日详情页购买转化率：80.00%\n"
     ]
    }
   ],
   "source": [
    "goods_index = list(Sell[Sell.columns[0]]).index(\"三星 充电器\")\n",
    "goods_name, goods_sell_rate = get_sell_rate(goods_index)\n",
    "goods_sell_rate.sort(key=lambda x:x[1], reverse=True)\n",
    "for i in range(10):\n",
    "    date = goods_sell_rate[i][0].strftime(\"%m月%d日\")\n",
    "    print(\"{}详情页购买转化率：{:.2%}\".format(date, goods_sell_rate[i][1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 全站商品来看，这款电商产品，在春节期间的售卖情况与平时相比如何？（参考第 4 问，可用 购买转化率 衡量售卖情况。）\n",
    "\n",
    "答：\n",
    "\n",
    "2020年春节的时间为1月24日~2月2日\n",
    "\n",
    "1. 春节期间，三星充电器售卖情况相比于平时波动不大。\n",
    "2. 各类商品的购买转化率在春节前后总体呈下降趋势\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_avgs(rates):\n",
    "    \"\"\"59天的数据取出春节前中后三部分，分别计算平均值\"\"\"\n",
    "    avg_spring = sum(rates[23:33])/10\n",
    "    avg_pre = sum(rates[0:23])/23\n",
    "    avg_after = sum(rates[33:])/26\n",
    "    return avg_pre, avg_spring, avg_after"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": "Index([2018-01-01 00:00:00, 2018-01-02 00:00:00, 2018-01-03 00:00:00,\n       2018-01-04 00:00:00, 2018-01-05 00:00:00, 2018-01-06 00:00:00,\n       2018-01-07 00:00:00, 2018-01-08 00:00:00, 2018-01-09 00:00:00,\n       2018-01-10 00:00:00, 2018-01-11 00:00:00, 2018-01-12 00:00:00,\n       2018-01-13 00:00:00, 2018-01-14 00:00:00, 2018-01-15 00:00:00,\n       2018-01-16 00:00:00, 2018-01-17 00:00:00, 2018-01-18 00:00:00,\n       2018-01-19 00:00:00, 2018-01-20 00:00:00, 2018-01-21 00:00:00,\n       2018-01-22 00:00:00, 2018-01-23 00:00:00, 2018-01-24 00:00:00,\n       2018-01-25 00:00:00, 2018-01-26 00:00:00, 2018-01-27 00:00:00,\n       2018-01-28 00:00:00, 2018-01-29 00:00:00, 2018-01-30 00:00:00,\n       2018-01-31 00:00:00, 2018-02-01 00:00:00, 2018-02-02 00:00:00,\n       2018-02-03 00:00:00, 2018-02-04 00:00:00, 2018-02-05 00:00:00,\n       2018-02-06 00:00:00, 2018-02-07 00:00:00, 2018-02-08 00:00:00,\n       2018-02-09 00:00:00, 2018-02-10 00:00:00, 2018-02-11 00:00:00,\n       2018-02-12 00:00:00, 2018-02-13 00:00:00, 2018-02-14 00:00:00,\n       2018-02-15 00:00:00, 2018-02-16 00:00:00, 2018-02-17 00:00:00,\n       2018-02-18 00:00:00, 2018-02-19 00:00:00, 2018-02-20 00:00:00,\n       2018-02-21 00:00:00, 2018-02-22 00:00:00, 2018-02-23 00:00:00,\n       2018-02-24 00:00:00, 2018-02-25 00:00:00, 2018-02-26 00:00:00,\n       2018-02-27 00:00:00, 2018-02-28 00:00:00],\n      dtype='object')"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sell.columns[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "name, goods = get_sell_rate(goods_index, True)\n",
    "avg_pre, avg_spring, avg_after = get_avgs(goods)\n",
    "avg_list = [avg_pre]*23\n",
    "avg_list.extend([avg_spring]*10)\n",
    "avg_list.extend([avg_after]*26)\n",
    "\n",
    "\n",
    "plt.figure(figsize = (10,4)) \n",
    "plt.plot(Sell.columns[1:], goods, label=\"其他时间\")\n",
    "plt.plot(Sell.columns[24:34], goods[23:33],label=\"春节期间\")\n",
    "plt.plot(Sell.columns[1:], avg_list, ls='--',label=\"平均转化率\")\n",
    "plt.title(\"{} 详情页购买转化率\".format(name))\n",
    "plt.legend()\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "goods_avgs_dict ={}\n",
    "\n",
    "aavgs = [0,0,0]\n",
    "\n",
    "for i in range(goods_num):\n",
    "    name, rate = get_sell_rate(i, True)\n",
    "    avg_rates = get_avgs(rate)\n",
    "    goods_avgs_dict[name] = avg_rates\n",
    "    for i in range(3):\n",
    "        aavgs[i] += avg_rates[i]\n",
    "        \n",
    "for i in range(3):\n",
    "    aavgs[i] /= Sell.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x216 with 1 Axes>",
      "image/png": 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798oiSrNoQvjTAP7dWtvoGbBa7/NVCL3TXyPkliO3BvABa+2PTFixwvakAPwC/QoqShnAl2fwzMu9Z1THJQuh9Ky1PzDGXI2w8uTrAN7uXSODqNH7PkLjRY79YUxfhKTnbtfq0d1BzPyi10SeCNLzxAattVvmPHjh723mSaVs63XzAFJxdMcCXT8AMNIzxDtynQFrbRxA7jSJa1OvP1s2pix1O65fsNbW5j4ykflIAuaJJJJIIruBzDf7/zVjzC9NuKpqtuPONeG+HYkkkkgiiTyOMieYm3CpbMqGy2/3Mt5udXLc8wHsYcN9OxJJJJFEEnkcZT4J0KMQLgAAwhrTIxHWPDsx4b4dXwFwpQlXOc64exwALF++3K5Zs2abG5tIIokk8kSW66+/fpOdYUfP+YB5Cf0a1AlIfa7I2xEuOvgUgP/PGLPaWvsFPcAY41YMrl69Gr/7nV/9lkgiiSSSyGxijPnjTN/NhzOfQn+zovIM5xwC4PxeudgFCDfniYi19nxr7XOstc9ZsWJbtopOJJFEEklkLpkPmF+PkFoBwmXfD8Qccy/6e2s8B+HGUokkkkgiiTxOMh+a5TKEq8r2QviGkGONMWfa6F7YX0P4BpxjERbtv3HBW5pIIokkksiMMieY93asOwrh0uxP9aiUm7xjJhFu45lIIk94abVaePjhh1Gvz7oVTyKJzCj5fB777LMPMpn5L2id13L+3t4jF895YCKJJIKHH34YAwMDWLNmDTd7SiSReYu1Fps3b8bDDz+M/fbbb97nzWvRUCKJ7O7S6XTQarXQaDTQarWwIyuj6/U6RkdHEyBPZLvEGIPR0dFtjuyW3EZbnU4HqVRqZzcjkd1Aut0uOp0Out0urLUwxiAIAnQ6HVhrkclkthuQEyBPZEdke/RnSYF5t9tFq9VCu91GOp1OQD2RbZaZADyVSiEIwkA1lUqh1Wqh2WwmejZPabfbzgAmsnNkSYF5EATIZDIuJKaXnky2RGaTbrfrQFwBnCBOsdai0+nAGINsNotWq+Uol3R6SU0VdDodBEEwzcOz1qLb7brn/tSnPoVvfvOb2HPPPSPH3X///Tj77LNx7LHHYj5y8skn45BDDsHb3+7vaBuVgw8+GDfeeGPsd+9+97txyy23oFar4YQTTsBVV12FsbEx1Ot1HH744finf4p7PWgilKWloYAD706ng3a77UA9nU47zyqRRHwAB+C8bx/krLVot9vodPq7uhpjkE6nYYxBu91Gt9vdIdrl8ZZzzjkHl1122bQ50e128eY3vxknnRS+2yKTyeCAAw7As5/97MhxP/nJT2Y1YOvWrcPrXvc6ZLPhNvIbNmzARRddhPPPPx9AWNHzm9/8BgDwkpe8xPXt/fffj6OOOspd54orrkCpVAIQjs/HPvYxPPzww2i32wiCAJ/5zGewbt06XHNN3HssElFZcmBO8UG92WwiCIIE1J/AQs+aQA4gohM+EBPsCTSpVArpdNp93mq1nBff7XbRbDaRyWSWhH599KMfxUc/+tE5j7PW4sADD8TRRx8d+fz++++fNQm8xx574Nprr0Umk8GJJ56I0dFRnHrqqbjiiivwspe9bNq1/vCH8MVBhx12GH76058CAI466qhIX65duxYPPPAARkdHcccdd+BVr3oVLrnkEqxYsWJOjz+RJQzmlATUn9gyE4ATdOM8aQVxeuCpVModS53qdrvOKwcQ8dDnTbt84APADLTCdsvBBwOf+9ychzUaDeRy0ZdcNZtN500DwF577YUrr7wSv/jF9Nd+Ll++fMZrG2NQq9Vw2mmnIZ/P45RTTkGtVsMNN9yAf//3f8eFF17ojh0fH3fG4o477nB/33TTTa7P169fj5NPPhnFYhHGGPzyl7/EmjVrMDAwgE6ngx/+8If44he/iKc97WlzPvcTVZY8mFMYQnOSNptNNykTUN+9hLwvQRzoe+AKyr4oOMeBuC9BECCbzbrzaDg6nQ4ymQyy2ewuTbsceuih0xKSo6Oj+O///m/3/8qVK7FixQp8/OMfx3e/+12MjY3hHe94B6y1jv6YSX7yk5/gnHPOQTqdxle/+lVkMhksW7YM5XIZZ599Nk49NVwkvmzZMvzkJz8BEHrm/FvplnK5jHPPPRd33HEHvve97+Gcc87B5s2bceedd+Koo47CPvvsM2d7nuiy5MCcoW7cJNIJyknHJCn5z0SWpsQB+HwAGdh2EPdFQZ2OQr1eR6vVQqFQmD0BPw8PerHkpptumvX7O+64Ax/+8Ifxl3/5l/jBD36AG264AZVKBd///vdhrcVTn/pU7LXXXjOe//KXvxwPPfQQ/vZv/xbf+MY38NOf/hSbNm3Ccccdh02bNrnjZqNrOJZ33303TjvtNAwMDOCII47A8573PKxfvx7pdBqXXnopstkszjrrrG3sgSeWLCkwb7fbqNVqqNVqSKVSyGazDqR1YsaBeqPRSEB9iQkBnCAK9MeWiczZZEdB3BfeM51OO0CfmppCLpdDNptdchHgQw89hK997Wv40Ic+hCAIsG7dOrRaLdTrdTSbTRxyyCGznl8qlZDP5/Hoo49ieHjYfZ7JZCLVMRs2bHBe+O233x7xyAn0z3rWs/CkJz0Jb3/72/HHP/4RP//5z/Gzn/0MZ5xxBg455BBcccUV2H//uN23E6EsKTBPpVIolUqu1rxWC98Fy0nqVyvoBGa1QuKp7/qiHDhLCecL4Dyf1zDGIJPJLGj5qjEGuVwOmUwG9XrdrRrNZrO7RJ31t7/9bZx99tkRblyl0WjgYx/7GN74xnA/PNIe3/jGN7Bp0yZX6TIf+fjHPx5JntLTPuecc/CsZz0LL33pS/HYY4+hWCwCAJ773Ofif/7nf9x4nHvuuRgaGsLb3vY2BEGAD37wg6jVavibv/kbpNNpHH/88ajX6xEDkEi8LCkwZ2VBoRBur95ut90PAZ6eF3/Um0qn0w7UWWu7I55aIgsn81nMM5cwCc7zFxrEfQmCAMVi0W0D0Gg0XPvZhp0hxx577Lzqw7/xjW/g/PPPx+DgIIDQg261Wg7cJyYm8OEPfxivf/3rp5370EMP4S//8i+x11574Vvf+hYAYL/99sPJJ5+Mb33rW1i/fj2uvvpqAHBADgAHHXQQDj30UIyMjMBaiwcffBBXXHEFgHA+/9d//Rfuvfde3HzzzWg0GvjpT3+KBx54AF/4whemtSGRqJgd2YNie+U5z3mO3Z43DXU6HVSrVVdNQCDW6gQ+D71zegr6GT+n55aA+s6RhQBwIAriPH++IE7g1b/nGwH4z9JsNtHpdPDAAw/g6U9/OgBMowB3Jel2uztEDd1zzz148pNjXwm8XdJoNGKTytZaF/k8keSOO+5wekQxxlxvrX1O3PFLyjOn6OpPTl563j6o8xjlX3XycouAVCrlPLlddfLtDjLf1ZjzER/E/aX3BGcfrP3P2SY1KplMZpv0IQgC5HI5V5vut2FXBPUd5fgXEsgBTCujpHBFbiKzy5ICc3KnTIbR6+ZvAncul4tUs9ADYRUMJ63WJnMvDlYuzLTQJJFtl21ZjTmX6GpNjaw0QlOg9oX34jF+bTrQ30GRewDRSMwFfnGgY4yJGA/eP9GrRBZalhSY62IQ5UZ1tzt6a/TUAESAXcNwenFa9sYqBQBuIivoLLWKhZ0lWpOt1NdcRnImT5rjo+POJDZBHejnVTRv4l/Hbw+F39EhYDWM7gFEUJ8NjP37+gbEPzaRRBZClhSYt9vtSCkYE5+clNlsNrIikCv8SKHwO02W0qvj36wnbjabkesTxNV46O9E5l5Oz2P43Uy0h39NvZ5Ph83k6Wo0oHXpNAJ6jIK7UnL0tNUwKbU3GwXjAzh1ZaZn3RV0aNOmTbOu+qQsxDbU890IzJdKpbLDi4fGx8dRLpeRSqVm5P07nQ4eeeQRrF692n12yy234MADD4zd72ZXcPJ2fgu2QbrdLmq1GsbGxjA5ORmhSBqNBmq1mltuzTIxbpTUarWckugGSs1m031HCYIA+XwehUIhsixcvXlek1UM5PH1Ok8EIe3B/m80GhEeG4D7vtFouP6mQfVBP51OR/hqAmc+n0epVEKpVEI2m414yPSC9QUTNMYA3DV13DlejPZo7FVPCL5Af98WzbPwPrpBl4oaG5/SiaN7FroY4d5778XFF1+Mk08+GT/84Q9xyy234PLLL8dnPvMZrF27dtr9XvnKV+LBBx+cdh32LeXUU0/FlVde6f739f7Nb34zjjjiCBx11FE46qijcNhhh+H444+PXPMLX/gCjj76aBx99NF45jOfif333x9HH300XvKSl0QqV6ampnD44YdHrn377bdPa+N1112HtWvXur+r1SrOOuss/M///A+uu+46bN682R17xhlnuAqcz3zmM6hWqwDg9AUArr/+evzDP/xD5B5r166NBe1vf/vb7tg3velNuO+++6Yd02q13N+f//zn8e1vf9v9r/fdEVlSnnkmk8HIyAiq1aqbRMx+kyLhHtS5XC7Cg2qYDsCBhHpdfiWEv+8LjyEo6CT0F7ZoYm9XTH7NV+JoD60e0mdm/ynAxtEePD6uT/wdDGfakkE9dgVCv9+1vT4tR2BvNBqR+9Eo0Lj4QKzC+2ukpxJH9cR9rv27UPpy9tln48gjj8TatWuxevVqvOpVr8J73/teHHHEEXjzm98MADjiiCMwMDCARqOBu+66ywEiEK62PuGEE3DAAQfgpJNOQhAEWL9+Pfbdd1/ccsst+Lu/+zs86UlPQrPZxEc+8hG86EUvAgAUCgVcfvnlzst/4IEH8OlPfzrStne84x3Yb7/98LrXvQ7f+c53cOedd+LUU0/FRRddhKc85SnuOEbcQOiV33DDDZF9X0444QTss88+OPnkkxEEAT784Q/j2c9+Ni688ELsvffe6Ha7OPHEE/H9738fAHDXXXfhm9/8Jg477DBceumluPbaa/HII48ACKtpvvOd7+ATn/gELrzwQmQyGRx88MFYu3Ytvv3tb+Puu+/GUUcdhXa7jR/96Ecol8sAgC9+8YsOnFUHPvzhD+PUU0/F4OAgDj/8cBQKBQRBgAcffBCrV6/GeeedB2stqtUqrrnmGldyvb2ypMCcVQZDQ0NoNBqo1+tun4xCoeA8LnplnIwKJNwVz/fEOZFmo2DiNvNSztWvmFGLq7TMrkLNzLfag9/FVaH4yeLtTfDFgbi/sEvBW6kT9imB1ufX9TgA0wxRKpVCLpeLcPDUJa104rW1zQruXPPgc+XaH3GATlD3x2BHQT0IAtRqNdx2220YHBxEJpPBG97whsgx1157LYBwL/ETTjgBb33rW2Ov9eMf/xgbNmzA8ccfj0suucQt2//+978/rY2tVgsXXHCBA7vNmzdP8z5TqRQ+8pGPRErvGo0GTj/9dPz4xz8GAPzoRz/CP/7jP+L222/HC1/4Qrz1rW/FBz/4QbfY6bTTTsP69evxgx/8AC960Ytw4oknotvtolAooFwu49e//jUeffRRnHTSSRgeHkatVsO73vUuZLNZHHbYYQDCLQ8OO+wwtFotPOMZz8DQ0BA++clP4r777sNpp52GpzzlKchkMnj2s5+Nq666Cqeddlpks7ILL7wQjzzyCPbdd9/I851++umoVCquhv8Vr3gFDj30UGzatAn33Xcf/uRP/gT77rsvHnnkEdx33307DOTAEqsz73a7blk+wZVLj0mt6BJ+IGop4wBKP4vj6eI8Q4KOlsTFhV/qyepiEmBxvfeZwDnumX3xPWiNPPw68IXgCWcDceXgtc0K3gq+PoDHVcyoF0+Q9vl8NQqkhay1EefAXxykpZV33323293PN25+3/vjruM00zHzlXe/+9048MADcdttt+Ev/uIv8NnPfhaZTAa1Wg1BEOCUU07BS17yEnz3u9/Fu9/9bjzzmc90546Pj+Pwww/H5z//eQAhxXDjjTdGFgABQK1WwwEHHIB/+7d/c5/97ne/w/r16yPH7bvvvjjooIMin1166aW44IIL8La3vQ133nknGo0G8vk8/v7v/x4A3DYcr3zlK3HllVfiBS94Ad7znvdgZGQEf/EXf4Hjjz8e73vf+3DAAQfgjW98I2644Qa8853vxMtf/nJ88IMfdPTMJZdcgvvuuw/3338/rrvuOpxyyil46lOfCiCkZJ773OcCCGmmD37wg7jjjjtw3nnnoVar4dRTT8UnPvEJ/OxnP0OxWMTy5cvRaDRw0kknYb/99sNxxx2HbDbrjOKxxx6LYrGIIAjwla98xY1brVbDAw88gOOOOw6VSgX7778/LrroIlx55ZV43eteF4s9i1Jnboz5GoCnA7jSWntmzPdpAPf3fgDg/7PW3jKfa2+rkK+kl57P552CVqvVyD4ZChLkTH2OUn+TL1VAo5enFIy/7wt3aPQ9SQVsik8PbKv3Ppsn7X+vMlu1h7aVfewv5lEOeyFEqRpGTIyQyKXHebBso27PoMdpBZJ6vZoQ90sN1ZNnJVQul0MqlUKhUEA+n0ez2XT5AK2qYn9rCSzbYq3FP37/NtyxbnJB+oxywF6D+NhrDpzzuE6ng5e97GUuf2StxQ9+8AMcf/zxOPXUU7FmzRpcfvnlOOWUU7BmzRp84AMfcOfefvvteOyxx9z/2WwW3/rWtxyo7b///vja176Gt73tbfiP//gPAKEH/rrXvc6d8973vhePPPIILr/8cvfZd7/7XYyOjgIA3vCGN+CQQw7B73//ewDAa17zGhx88MFu7HWMisUifvzjH6NQKODkk0/Gy172MrRaLeTzeXQ6HZx++uk4++yzcdxxx2Hjxo0YHBzEPvvsAwDI5/NIp9N4ylOegqc85Sk455xzcOaZIYwdf/zxOPPMM52juHXrVlxwwQW47bbb8NBDD2H9+vX48pe/jPe///34t3/7t0jy9d5778XXv/51nHjiiQDCFbP33XcfXvrSl+Kss86KzN8777wTF110Ef7pn/4J5513HtauXYsTTjgBw8PDGBsbwzvf+c55jPzsMieYG2PeACBlrT3cGHOuMebJ1tp7vMOeCeA/rbV/t8Mtmrs9yOVyjusE4KpQms0marUaWq1WpOKFE77b7cbu8aGAqOWNfikcAZ2GYXs28/ITqXE8tILoXByzfjfTD4+ZTXRTKv95FzJTr/dRw0Jj6gM4ML2PfPCeydD49/LHRukWGlWOJ1caM9rL5XLI5XIuL9NoNNz91WjQeXDjh0Wg0+YZTFcqFRx33HGYnJzEF77whWk18Ndddx3OP/98XHLJJVi7di3y+bz7Lm4l5rXXXouLL77YPeOaNWuwbt06932328X+++/vdlC8+eab8cADD+CCCy7AmjVr8Fd/9VcAwr1gPvnJT7rx2rBhA6rVKn7ae2lFp9PB8ccfj7e97W3u2s1mEyMjI+h2u1i1ahU++9nPotlsolAoYNOmTbjwwgtxzz334Ktf/Spe/epX42lPe5rbhuA73/lO5Dme+tSn4tZbbwUAfOADH8Ctt97quOs1a9a43Rnf85734NRTT8Wee+6JdruNV73qVdi0aROazSbuvvvuyMZfl19+Oc4880xUq1WsXbs20nfj4+P48pe/jLPPPhvvete7MD4+7qKkM888E//5n/85bZ/57ZH5eOZHAbi49/fVAI4E4IP5YQD+3BhzBIA/AjjOWrswKVpPOGmy2Szy+fy09zQGQeAqJ3K5nLPK9KDJsSvXrcCh4Q4BRsGdVQzKmRPU1Rj4dAEwP9ojjuLQzxVgdxRoF2o5/XzvRc9XSxP526edgOkLboD42n9f/I22dOsHpXU0MtOxVHql3W47z5Y5AjoJrGKiofDbDACnvfrpsTQKj/U/m63f9bp+bX2cbNiwAddccw1yuRw2btyIkZGRyPfPfe5zccUVV+CBBx7Afffdh09+8pPuu/HxcbzwhS+MHH/EEUc4A3fYYYfhjDPOwKGHHur2YPHbEeeAGGPwspe9LPI2Ik2Aqjz88MO46qqrcPPNN+PYY4/FGWecgb/+67/G8ccfj+OOOw5vfOMbkc/nsWLFCnz4wx/Ghg0b8J73vAfr16/HZZdd5t41etttt7lrTk1N4fe//z22bt0audfWrVvx/ve/H/vttx8uuugiXHvttbj66quxefNmfOxjH8MXv/hFDAwM4PWvfz3++Z//eVpfr1q1CldccQXe9773uc/GxsaQz+dx6aWX4g9/+IPzvjdu3IjJyUnsvffeeP/7349Wq4Xly5dPq/jZVpkPmJcAPNL7ewJA3D6U1wF4obV2nTHmiwBeCeB7eoAxZi2AtQAitZvbKgyVCZhULp9S0VI57nCn3i/L0WabPAy3gWjVAkGdP8rhKk2g1Ix/H1X0uWgPnw7yKYptWdS0kKsxZxMCj4Kev2IzbqdLPV8rSHyePO5+HB+lhuJ4dT2H19S8B0shqUekV3TLZd24jaAPYEbg5nez1ZoDfZCOe0aNAOLO13Pq9Tra7bbT3y996UuxG2ZRnve85+EHP/iB+/9nP/tZ5H/KihUrcM455+C8887DS1/60mkJ4R/+8Ic48sgjMT4+juOOOw7WWrzpTW9CLpfDPffcg3POOWfaNWfqixtuuAGPPvoobr75Zuyzzz548MEH8YUvfAEHH3wwrLXYuHGjoz1arRb+93//F6effjre85734K/+6q9w+umnA4i+BKNQKGDfffd1m4lRzjvvPKcbBx10EP7sz/4MtVoN//AP/4BCoYD3ve99uOWWW/DpT38az3jGMyLndrtdl1ANggAbN27EmjVr8K1vfQuPPfYYzjrrrAiN8rnPfQ577LHHvF+WPV+ZD5hPAWCqtYz42vSbrbWN3t93AphWhW+tPR/A+UCYAN32pkb58iAIHJjSc+JWt8YYFItFFAoFR7uQgzPGOO9a69HjEhBxnrTSIwrsnGjqNdITJbAwZFdAm4+o56d9Md+ySKDvrWoScSG2LPD7iB4xK4rYLnq0SonEcdp+lcpcUYJGQwRnjZT0uuxHTXT6+7lon7N/qGe1Wm3ani1KtfnGgu3z/1dQ12NmA2f/87lA/Ve/+hXe9KY3AQjfwXnNNdc4z5fb9lL8SpPf/va3eNe73oXPfOYz7rNmswkAOOaYY3DMMccACF/q/NrXvtZ5lJ1OB8cccwy+8Y1v4Oc//zl+9atfodPp4JJLLsGaNWtw1VVXxS74ocH05TWveQ1e85rXuP9Xr16N1atX4/rrr8drXvMavOIVr3AJ2bGxMbz4xS/G+eefj9/+9rfunPPPPz/yooxqtYqbbrpp2jtPH3nkEZczYPKaeLPvvvviwgsvxBVXXIGzzjoLL3jBCyKJ4Eql4v5+61vfipNOOgmdTgfFYhHnnntu7PMuVG25ypzVLMaYtwNYaa39jDHmHwHcZa290DvmYgBnAbgVwI8BnG2t/cn0q4WyI7smshxROUou8igUCshms5EwG0CknJC8FD0qgrzPp87lHfGHIOJXZBC4AThgI9hoLbaC73w865lEvXYFOP5P4NrezcQ0OvB//HJM9ju5Zu0LvZ6/WpRgO59Kmbg+14U+PsCnUqlIO+NeVKEg7/ePRmMAIqDuP5NWs/hAPVv0FXfsfPRhJkDXZ6hWq9MqURZCarXagpTVLZbU63XkcrlIX+xImxeC256vLEY1y2UAfm6M2QvAMQCONcacaa1VgusMABcCMAC+NxuQ74gQwBk2A4jw4fV63VW45HI5N+E5oRnyG9N/uQAnqc8bz5efJveuYT4TsfTIM5mMS9oSfHgP37PW77Qtc4GvH8LzOv75bIPvvfuengJ1nMGfKTQmwMW99DiuTlwjhPkAl1JMQLSUkXqgxoRgq4bbT1ArVcJz/bcHqR4Q1DX/wn70E6IK3DPlR/RH9VsNMY+PE3/8dHz43WIAOYBdGsgBRJK6lB1p8668e+OcYG6tnTDGHAXgpQA+Za1dD+Am75hbEVa0LKrQM9fQttlsOk+r0WhgYmIC4+PjCILAeepKgWhInM1mUSwW3cShZ6h7d6iX5wO9go9ywLpvDKsflN+PS87F8eJ+ssy//0wUBUvnZvI8lQrRSc9z454xDohoPBgZ6Q8ljqNXemO+EcJMSU39js+h36nxUnDWRUFsF1cbsjJJqTi2Vek80nWdTidSg65jxr7TMfQNpnrgOg5KXfH/2Qy7Xt8fs/k4BInsOjIXYxIn86ozt9ZuRb+iZaeJtdbtaEjKhCvzCN7GGNTrdffC3U6nEwnztbZYk0SsYtDKGO7togDrg6yfmOMPPXJ661yxCiDitepWq3FUhE9vMNLQtqgR0nrtOGpEE1Zxi6kUgPwyQH1OPUfvy75Viof9tD0cvV8eqsbPj3T8qiLmKzRZyTFnu2hk2Rfdbtfx+5rk1lWi1D166Vrznk6nkc/nsXnzZoyOjk4DdLbVB3QanDhP3Y+C5gPqcXqUgPrOF3+ezXTM5s2bY6OK2WRJLednYT93NiQ9okut0+k0hoaGMDQ0FHmLOicLhX9zBanuAcENlEiP+B6n70XPtvBHy9kYSWgVDMN3feGGlrn5HrAxxvG2GhlYayMGw+fjtWrDBwx9JgUmrQVXoNYoRveIj6OrtqdKJo7z1qjKr4xRCkzBn581m01UKpVp9ItGYFquCPQ5cVJxzWYT1Wo14kQo9aKbd61cuRKPPfYYNm7cGPtsMz1zHP0y13k8fj59uj3nJbIwEpfTmE3y+bxb9DRfWVJgDoSd0Gg0nPdM0RCXe7Wwzpwg4HOKWldM2oHlbFw4ohSJz2UTZNTrVVqB7VWAZ7u0ZA/oGwEFGrZDF7/wWPWQ+TxxFRIKpAoWfpv1OxoxHyDJR7Od1loXycwnvzCXxIE4jQWpDxoKHQ8gWseui4EUwHXnxCAIIklb9hWvRYPLCK1QKMBa6xwEpWBoaAjqnU4He+65Z8TY+c+p9A8pHNKI1AluGKcrmhltaATCts+0rYTKTJFOIgsvMzkl21vkMJcsOTAH4DxzIMoTspPq9ToqlYqrF+bxujqPE4oTWjlWetJMrmq4rgBKUYDXxSnA9D2z9dhSqRRJmmoOgKLerV/Sp4YiLnz26RUFOQV734D4VAv7TWvute/8CEE/n4/3F5fU9Plw/dznk3XlL58JQGS7B22Lbons153zh/fh/TkRfQqGDoCuW9DogdGi5geUe1f9SqVSKJfLznAxomA0yoiAoE7jxDY2m82IQxDX9/yO58RtLJfIjolPAc601mShZUmBORVVa831c/4ulUrOi6LHzYmgNAApC92BkRNZE1/dbriPOiezlhbyWn47+TsuiajcPM+nAhDUtdySPKzed76i3jfQr/7QdrIduuey0khURI0M8vm8e7bZaCefa1eFVqNAY8n7aGJS+5BeJQGPe9gz8cyoQheEaVmheuu8tvYB/9bl+myT1qYrBePvrshzdMUr+129YDXSvtddKpWQy+Vc9Far1ZzhYDTIezLCJEDzWrOBCL/zI5oE1LdfZqu0ejxkSYF5Op3GwMCAS2QRfIwxkVCZSp7P553Sky5RBU+lUigWi85TqVarzhPiJDXGuDpVAv9cpXS+RxxXmaIAoQZBVxmqx8XcAL+f6b5xidr58Nc8l9U3atgUbNXLVKPl11v7AK+0U1xiFIB7Vr90UqthOC5cxJLNZh11xXbxeCY7/dp3v61+ZKN977+sgrqmFUPUM1Iw/M7fUldBXXWHPLwPrOl0GqVSyRl5/jBhrtElnQyOF40Wowptv4rem4ZAI5EE1OeWOKdkZxjEJQXmzWYTGzZsQKlUcvQJJ5tOWE4aKjk9cnp8BAL16PL5PMrlsls1yooY8rS5XM55/HH8F+/vg6lOIIIEQVMTjAQTfsf2AWFCln/T8+SeMzzOB0ht31zhHYGIbdJqDQU53kM9eL/9/t8U7XtNoiroE4y0usSnxOiZ801QfK0b+82PbjQXoREQ+1OTtUrh0HAprUGDRGPDxDkBmmsbVMfImfPecW+8ovigTmcinU6jWCy6zeT4jBx/JvE1ItGISR2TmUBadUbzDo+3d7mUZKZy2Z3VV0sKzNvtNiqVCiqVCnK5HIrFokvU0bsmZcIJzBVghULBAbwqO3nJSqXiEl2a1KrX6257XeUtOaHpgSnv7HPayj37iVGlArScj795PCc3aQWgD+w0Rj5n79Md+pvX9EFDq3fizgHiPW+/GkOPVX4d6JdE+rsnavWJGkWg/55FbVu1WsXU1FSkkoSiXirb4ecK/CodRgZBELh+JbWmCWDSIYzWeH+N/JhY9ytrNDKh9+0DwFygTmNBHSTFwryBvlbP99Y1B+BHKWwfdZrjo+8QWGzedymIn0SeaTuQx1uWFJgDcFxgq9XCxMREZNKxWkQXhLBqhHudc/9jTgJ62/TCuSiJA8S3tDebTUxOTroQmhUG3G7Xf1+oUgLKNfvJOF+Ut1UOWmue/fdpZrNZx+n7xoX3BaKbhal3SsAiONGrVPHzAz7frtGB3kefXdtDY0tvlqChWx/ofejNqrFSr0gTkErPqEHUdij9od68Ujzsl2KxiHw+j2KxOC2K8fuAQEmw5zP7Bk8dCgV1FYK6Uj5sf6lUirxVi/ckvcT5oCDMZ5pP8pN96jsii12RsavKTJUp2wLi1BE/al0oWVJvGpqYmMA999zjQIXlWQQFn1Nm+ElLysmhKwE5KJpUiluYxAlPcFalVkDhtf1qkbkWzMSBuHqmvJYfVTAx5nPvvpfu/5Be0n0rlD+Oa58CqAK3tt9vM4AI+PoUjO8t0yD5CUSloXhN9inPYRSg1Spst5YI+kaSx+iCKz9qMMY4YNdFZtQLf4ziwJH944+T0mGzAaVfQ88okAZe6So1ngBiP/MN7mzedxyYxXn2u5vMBOLbAsaaw+D4cG3MtorZ0TcN7SqSTqdRLpdRqVQiFQTdbrTahC8SIK9crVad4jJBpRxpt9t15+RyOVceRu5QPVgA0946EwRhOdrk5KQDbu4P4094pWDi+Gg/aelTEAoQSvnoS4nVGCjPC/RXnxIISCvovdke/lZwVhDSSEPbr0ZLn4c/7Fcm8vxr62ZqGjX41+Q1/F3o1LDSuKp3qUBP+k3Bnh4vcwO8HyM3PjujMU1Ya7t0SwXfSFKv9IUmNBAz8dRaVkidUmNGUCfg63oBrXRR0GbOQfuHjpJPtWnVDA3hTKuXl7rQoG9veaHOv7i8zGLIkgLzdruNLVu2uMnNSc+O5t4s9LIYGtNb73a7zgOnwlKhOYErlYorAwSm0wtqmUkLaP2xHktF94EojmvWwVYvKg78fU6agEJOnZwu28r+0Emq4K1t0esSFP3EmVJJPo3ht80/loaTx1KxGWkQbOjB+LkEjgHBW++vbaOHXq1Wp9EsrD9XT1WNJh0CRmlMgmsfWGsxNTXlPo/rH02CEvD4vDRi6XTa6R3BkvoUt1kZMD9QZ7v5Np5CoTCNZlNw2ZbyRr2/0jVzRZ9LQTS3AGxbeaHvxfN8PyJaLFlSYE7PiDsSMtwlzUDqgJ1aqVRQr9dRKBQc78mqCiohO5s7qWnVhQ6i76Uo+KonRG9MQ1fSAwrEfrhGgACmK4XPU3PCKN3BaxeLxQglZK11eQWlSdg2Xo//K7/qUzQU5Yf1fHqneqxSQPoMeh4QfYWbJkh5DmkUPgO9TTWASsX4eQGf7uIzMLmofZ9KhfX9g4ODGBwcdEaBlBaNIsta2Vb/fvpsSsUp6LHthULBVVABfQPA5Da3mlBd4Ln0IBXUBwYGHA03NTWFer2OcrkcWQznV7loAlepgZnKG+NAPc6rXwqilBOjkPk8g84FHXsdY42UmfegQ7OQsqQ480qlgttvvz1SWUAA1dpf5VBJx+hWtJz0uj81Sw85iLo4RekWP7wmdVOv1yNcug6wX+WiXhTBQMGEv30gVTD26RhOQCojJ54qqIbder4PhP61NaGnoEUjEtcm/q8KPZNya5KY/aE8MsfAryRRTxjAtEkF9OkgDXvVCMVx29qPrGPXJfXVajXyyjjSc9Qd/5k0KuF9dbL7UZi+mYl9wUjTN7Zss29gVeeazaaLIkgjalmrjp8Pxj5YzQbWPiAuBVD3ywvn22Y1dEDUuPp5L/6ve+Fv7za8ZnfhzLU2u1QqubC8UqlEQI+K7O9roTytcr7WWufxc/JqqE+viAPD++rLfjkJyc/rSkR6Ntoees38Tie0gicwnQPXJKC2RysxeA4VjKG37vnO42Yql+TkVIUHol6nKivBWT/neGh72bf0vNmH/JxGQxPOACLJURovHkMqhAaV9+Nv5aW1/JLGgv2s/UBvc2xsLGIM9SUozJ/U63XHvdObJvWnfcFnplHiGgj2GZ+RkZRSYLwHK4DUkPkUEytuaDiYa+IP6/QJ6hwb31tnn6mu+DSNRnj+AiRSNbtarfr2lBfOZPiAvhOgkSn7kMfrmoTFkCUF5uTAmTjSEjzydvSc6DFpuZpWtKgS07vUhBd501QqFVlKD8CBCHfR00EqFAoRb55WWL3KQqGA4eHhSGLKB0rfA2ZbeT9Kt9t14X7cYhvl57WUjRytTjS2G+iH0FR09ZAVoPQ3vWUA08oE2XcEPxozcry1Ws1dx+f8FUz8hUH0+BXk1dsFEMmTcMLpHjhKefDZ+Qx8zRn5Zxp99jfbRqMTt399LpeLRGk0utQ5esxKb3AMKfpsdEjY1/ocdBoUeNlHIyMjaLVaqFarrr30+DVKYNv4LOwT7VM13EpLKGWnOqO5rcVKAM4lPn2pc3c+5/gRnzokGnmqvnPO8XvO6cV4qceSAnNOZtaHA3B11lRoTjZutkUw0lKgarXqvCy9NtAHd+XMCdp+QoqTlxNIa5HprY2Pj7vKliAIUKlUMDU15YyEXpNVKRQFaJ24Pl/te05K56iXTwWK83qBPujRIOpe3epRs59oMNg+bWucAWBylooNwFXh+EaL19cFQUrFMEGo1STsD33+OE5Tr+97kepxFgoFt9q4VCq5bZVpkLRf6KlrlQePJfdN+k2Nh3rDuksj9UGjQX1G3pP9QA/Qj7K0VpxGYHh4OLLwiG0kVaS0nkZnPk2klS3UW+XWVdgf6qGqVzuf39srM4G46ocaTtUJrfBR3eGzKoBrn3HsVT94z8VKhi4pznzTpk244YYbIvw3y/+AaOe2221MTk46YAf6IBAEAWq1muvYYrHoqjbIbdE7VLqG19D/CcDKhykfqvwpvSC2z598/iZRyn2r4vF4P1rw+XV6oxQqotIInKSaGKYS6sIrHkPgJED5ExLoeyN6TY6PJtnYTv2Mz+tzjgAcCGiSWfl45Xl5jk8DaX/Rq1IjEQRBZGUp78eE6NDQkANurfEnXUavlgu7aLx8CoYAoxSFX/OtvDYNG+/Dz3ld6o0PFHF5DI6fGjl+x2vFXcc3iv54aSSg/UlRkNR2zAesZzqG462/Vah/HAOlEVV8o640ioKwD956P+qUzncaSHVOACxKnfmSAvMtW7bg1ltvnaYwTFzGAY21FuPj45iYmHChPIAIGHOglEsH+m8xJwfOEi8C+uDgoNvnmnx5rVaLlMKpZ6WbQ/nVCuRO/bbQyitvrG9YUvCm+IDuT0SdvPTCdYEVFVSNpoK2ct+cKARwGgICE/tS6RL1jPgsLDdV2gaYnizUxJNOXj1HAVwpCI63GjSlS6gvvNfU1BSmpqbcMvkgCHeLHBkZwejoqHuzlSbB2SaN8HSLXn7OvIXSQn6uQvuNn/tgqNGSRiqajPP1Qw2MtpXjOBuoaztmSpr6SUWl22Z6Ds2D8Bj97Y/nbLhFw+LnWhSE9Th/gZhWpSllEncff14xsqNx5XEaYZKO3R7ZbcB8fHwcN910k+scAod6FTr5NbxVnlWNASeceryakCQfr4t+1OPI5/OORmGIqll9/Z+gTrBX3p7X0AmhyuV7OTRaqqicDArgAKYpHL1t7iqo3wF9/k8jHd0wikDqJ/PUeyGQ81qaWIsDWeXcfS5Tr+Pzrr4H7kco7Ec9n9fXaExpFj5vsVhEOp12PHOlUnH0UzodvtFqxYoVGBkZcWPPZCzHWT046hHXR+iCGx1nenRqHNXgKS3Dfub9+Vzaj2rw+Jtt1c95D+ak9LPZDINWCCmw83P/OwVIzdMo9z4b1TIT/aL3U33UKI/671OVGvHNRfeoM6RRARCN/n294lgxj7I9stuA+caNG3HjjTdGsv3K+6lHoMkvrVDxaQMeq4rqexAq6hFyAyZOQAIyeUrl19g230Pk/ciJcvKpZ8rrq5em1/TBV5VMn4URxEwJNh6vnKAfwur19Yfnsc90AnBy+x6jJofUAKlHrxNcQV2fkccp9aSf07jwf05mGjP2j9JPbIPmQziu1WrVUXidTgelUgnDw8NYtWqVS0bz+dRgqn5xgjOhyz6mjlCfGEGyzTQ0jORoOPxxYPSoDg6pFd7H9xi57oI0okZqCuoziXrr6hApnz+TUVaHQ436fCiYuCKCOPpR5y6fSQGf4+MbrTgHTe/DsaWOa7/6URT1qVgszvlccTIbmC+pBCg3uwL6IK3KRc8E6C9f73a7LsT1927RKg5SKUB/5abWqvsVHEDotZTLZRdq875sh1ZHKMgzEtBKAW4QxuXitPB8Bnp0CrjqqWtWHYgmKKlQvjfh0wwaXqrwXOUf1RPh2BCg2Df0MPmjXnC323UgybHQiUKA4yTSceLzqDHyPTf/OfV8/9m1UoNt0I3NJicnMT4+DmP6FVKDg4Mol8vu+4cffhiPPfYYyuUyli9fjpGREeRyOQwMDER0iJ6oD7D0kpVa42pmpU9oANWLpzOgC8WstahUKs7jp7eoFB+pFjUMjUbD7elfKBSccSOw+9svx4EtgVl1hfOVoKoGU/Mlyjv79I0vNEDKiasjpPrLNvjcd5z++M+hkYvmZthOdVCYP2O7fN5dHY2FlnmBuTHmawCeDuBKa+2Zsxy3CsCPrLWHLFD7IpJOh1uAEgyZxCRIMnThACilwAmklAG9B63p1aQUAbVYLLoqFXLLWqbGiUWgUkrC5041GRIEQcRQaP230h5sC++hHqxfp6whoHLDnJScVJyUnOScSOq56a6T+v5NfxIYY5xHyhdFKCgRINQDVb5XPWqg7/Go4VRuk/f1K4uAKHfO/zmmnIwaThPk6SX7/c1SU01012q1SBtKpRIKhQIqlQo2bdqELVu2IJfLYWhoCCMjIyiXy5GkuR9Nsg+UAgyCYNqLKdjedDodeRMSxzKfz0eqg5TC4PwB+juP8hwFNjoUvI5uNMcoYGpqylX4UNdVNC/CdtBYaZTGfvV3b4x7UYdGZkoxUXd0zvl6oMZ6LiDlHFAQ18QpjYUfUVWr1YjnzhybVrFoZLsYMifNYox5A4DXWmv/yhhzLoB/ttbeM8Ox/w7gudbap812ze2lWR577DH89re/jSRT6IFriZp6BAoGfimeegI62RWEVQnplfEeXCDEl/v6pY6aRNL3lqo3xgmtlI8PshqCq0fKiUqgVmqCYEpg0My6et8+n8r+0KoWpX+U9mGb2Aa2mcrulxTyGn65JcWnAPwJqZ4Rx5TtU2rGp1qUN1Xjrl6yT1/40QyFn9Mr04VsGnlUq1XnDRaLRZTLZQwNDaFcLkcAUBNnvJdP/ahXrpGX6jTHlQl5jWw058NnVENP465zqN1uT3slH6NKVvFYayMrZH3qxB9bBUn1upVi4XjpGOs84fd+boB6o46MgvBsotGCUijqwVMXaNCob/o8asT0jWELKTtKsxwF4OLe31cDOBLANDA3xrwYQAXA+u1r5vxEN7NS60+FoGLS8iu14Idwmq3XF1oo91gulxEEgVvCTe86lUphYGAAAJy31mw2I/XEfq0p78VJAPSXrdMTYm26ArCGyUCUB9TwXIFNw05VduXW1UiwvZqs4mRhWE1jxYkOILI9wNTUVCRiYBKR19JkqyaqKEqNsG84cdVz9Hl27VcgWqGk9IoChvY/gU3pDk1mq4GhAeLmbIVCwXmKbCNXiXY6HbfislarYfPmzW6foIGBAQwODjrDpjy7AhkNjobzQN+54PPpfvyq/0A/iUkDzZWk1AFy+6R9qMOk/hhdcTwHBgZcUph7v2jiTw2+Aip1Wp+LxlSpMG6NwefkfFTHhMlgGhifOpwvgGsEzGvrPFPjr3tA8RjdHoTnx0UA6kzQgC60zAfMSwAe6f09AWB//wBjTBbAPwB4PYDL4i5ijFkLYC0ArF69ettbCkQmJhCtUaZHSFAsFAqOj9WMOnk/hv4E1kymv5WsKhwtcDabxfDwcGy7uN9FtVp14Rf5dA3h/QoXpXn4blN6ITQOXPjE+3DCUJFJMfHZlR80xkTemkRAI61D5fI5ZF2Qw3uzTfT6OMkI6pog428FYQKseq8EL+Ukfd5ew20ey2uox019UC9L8xh+VKHbKvAYXgPoh9YakSioc1J3Op1ItEaDb4xxNAbHnwBYr9exZcsWpNPhMvuBgQFnMNVjJ3hpP/Da/Jwe6uDgoOtP3ksrbNhvqi/qtPg8tdID7B81nqlUCkNDQyiVSpEVvRRej/NW6RX2L+/Lkk5+zmN0vUcmE67GJWWpeyHpwqnZPGE+t58s1TJgrTxR+pJ9reW11ENtt++c0NBrH2WzWSxbtmzGdm6vzAfMpwCwKLIMIM7knQLgi9basZk601p7PoDzgZBm2fam9vk89U40rGJnUqGnpqbcQKvXRiXOZDJuZeLU1JS7BhcZke7g4HPPdK2YUe+SCtBoNFyilnXkbBuBVb1hKj4NEPlIKoLuPNhutyMrTdWbo5esiSb2mXre6gErPaJASuDwkzaaNGSf6r2o4LqSlZNZPTXem4uotA85yenJE0D0RRq8Lz1qHsfxUCDkvdRYTk5OuvHgjyapOYG17JLHqd6pESCwayWDRoFcZq+AsXXrVmzdutV5wtzdk4Dhg67y0DTGTHTS2PPVcrw326zJUT4bAZHPzR9eUwGMoKTJcoIsOXUaaUZ1bKMmgH0PWp0GNT5+NFipVJwO0rNVA656pDriAzj1kXjg7+zJvlddUOqVhl3pHKV3Oec4t/U4OhKLIfMB8+sRUiu/BnAQgLtijjkawIuNMX8D4GBjzFettccvXDND4YRkpyowUdnUa9GwnslFioZZExMTqNfrkcRKqxW+lo5eAUFDB4XXUG9DgardbmNiYgKTk5OOW+REUM+HYS5pGhogrjoEohtX6cIkPpe2h8CqRgeAK69jX2pbqfD0vn3Pww8vtQ9o4NRLIfCyzzTKIUgo6GtlgoIQQYFt1hBWE758Xo0weKx68AQYTUTFecPss0wm43SCUQjQ99J0Kwm9BseBVJsCCp+N9yFVQMeCY+/X9nPM/UopXrfRaEzjbNXQKr/McSM9w/vSkDB3oBt2KRXEDdsIrCy3IwXH/ZE4FjRkfiTFeaqGROe6gjSfWXVQdVfbqfqh11WdoVCX+aOUm9KOPhXH+7Od+tpDnqO5HqVCF0PmA+aXAfi5MWYvAMcAONYYc6a19lQeYK19Af82xvx0MYAc6A86NzrSjgf6S2R1oDhJqHRctUcFAOBeBaZ0SD6fdwBbr9cjnHXvmZ1XqRYf6E82KjmvOTk56b4joKuHwQgBgLsfFUytPo9XUPA9aLaHXqHSJ7wm0PdM1NPghPWToArKujeJelraP+rN+OOinr4aSfXg2bccQ22jXov3JthoxKDG3k+I8b6+h+1PVq348JOnmoT2+WL2PcFaPXWCJMsMWQao5ZAEVa5fIGWj9fl6bx07rfTSSMLnsKnDtVrNgTr1jiBMQ8KqEo1aUqmU85g1ya4UDwAXmbDvCeqa3FSd0QQu9Yu6y3HWvleaVI1mHHAyGtVIUQ2NJjk5llqRo9E1yzjVA9dcD4Xfs23cW2ohZV6LhowxIwBeCuAaa+0OJzi3t5rlj3/8I37zm99EajjV2vFZ/ASQhlJUVNIZftJGAYHgNTExEQkfNavOUFMTJHG8L5NTlCDob71KoKXwWgrgfmKSk4MTjMpJRVIl5XOzzQqIBE1VZi3RU2+YYODTGOoZ6QZevoLzejyHohy26iMBM86TUq5UKTbluHWy8v6c3Orp+aEz+4Fgo960GhutbFEjooYqLgmt3huAyOIljiErYghO1Fmt2ScP64OXRqccK3qHfDa2VXUGmL5egNEM+5kArQ6Mv+sinSa2k/obFzGoLipdxuvpHIqrNlHHQyk2is4XzQHoWPNHKUn2tzowSqOqg6FOlz+XOJ/1GqVSCWvWrMH2iNnRRUPW2q3oV7TsNNHsMhB9iw2BSXlBpUQ4iEprcID9siz1MlOpFEZGRlzoqEkiXcjDWmKCDxOXGl75NcNUAuWnqRz0BHTS0dNjX+iqUfYDPR0CPH9IaSg4avhKTyMuJCVY8TrsHwVYAM571ImmiWQdK/We6VGpoVEvkhtTMaTmDw0Hx0kTYOp9KrXD4/i8alT5nH7ITceAxpjHqIenwucljwxM335Bn2VgYMCBPPurWCxi2bJlzvPl3kJMRHOfoFKpFEmO6tYUGpX4i+Z0LPS5uV2zVtHQY2e/Kr/O9qhDotGlP/84vroqm3rIuai0GH/U0eL8jYsOAUSiUI4vjZRGMMrT63tnOccmJycdaHNcOCc5v3gPzhcdf/7PtrPvF8MrB5bYClBOSA6Q8rEEBQ3PFfyVWuD3WinCiUlvWycow+BSqeR20+Og6qRlAotla7rYJp0O642Z2NQ6bHot6tnwzUdUYLaHRoETTjP+vAcVTCsFFEj4TGoAtDpASyGVl+R9lcvkvbiPCYX9qpUo9Pip/BxDDcs1pFZgZ8QzU1VEHM1DA8bJzLppXWZNHQL6ZaLqzXNcfGPB3IIaQ/UmeZwm0Fl7znvx2WjINGlI2iObzbrtApjfYUVMtVp1CU/qHvuaesXxpY761I0mKTVS9KteOM80aqMjwmuTMuJ51WrVjREQ3f2R7WDSX/clUiqMfUQuXpOKbIPqAQ2v1vnruPnzmwv/lGYbGxubRp1wjHUhkM8IaJvU+OhzUScWQ5bU3izr1q3Db3/720gtubXWec0KsgQtBT5NBgLRkF+TFjrwHAQqCicneUIey/sSjHWyANF9K+gVKp3CdhIc494ar+CjXrR60wQjUkj6ua98OokJXvoKNB909VqqnL7wunov5fbZT0pFKehovTsnnj6LGmC2Qz0hPzKLm3BKR2j/0gNXjlijOuoF/9ZyUz6njgMNngKNrvrV8eSYat+zXaxyobHudDqYmppy3iOfpVwuRyo1aHSpn0oHUhe12ot9plEWn4fGTZer8zut/6co0Ou9fC/dT0BSh5RG0ujBN+g6tmp4ObaaBKUuMx+mxkPvo3kt9gcNMPtJ9UxxQ6NBPZZ9lM1msWrVqti5M5fsMM2yq4h2CJOgtJLLly93E4uWlpMAgFNABUSCvlYN8DjlBfk/ABeG0ZPQsIvKS6XudrsRLls9HIK+hoHqCWlpn09LUJE1IcT2M/RlAo00i0YrvB4VTl9M3O12MTY2FlFqTT5xgvhGUhVbE2FMevE49iM9IN9z4bNp9QPDYD4jr6URGa+pfUQg1dWJNCC+J8+xtdaiUChE3ixEeoXP7k9cNa70fnk9NaJ8rmw266qUGBVR6FXyGSljY2POqPGVb1yfQBqkXq+7cScYkw5TffY9anrK1FMaU75GMZ/PO6DjcyiHTl1QY6V5BH7O43wDq0vjte+oC4xOdFWlPw5K6TE/pXriV5v4lJpSJ3SEqLtqHNk2zUPF4RSF91JHc2hoKPa8HZUlBeaVegMPjrcxmusiE4SeNOteK5WKA1hafypqp9NxoK1KRRDlBCRokdOigmiii2Ckis2JnM/nHZdbq9VQqVTcfdXzUAuvwGKMcSWSWmbGUJTnqoIy/FSvjkrORUfqPQDRiZLJZFw4rGConoyeo56M720rtaK5BbabHjQnsp6n3g+BQiMD9jujlzgPSduvNJQ/vhqaqwen3joBBIC7BqkoTdJpUl0dAvadto1rH6g3BGWuMvapQSZGtRKG9dakV2isuPiI9MzU1BQmJiYARNc6KMeskZECnOZQaMwYGbBPmZdhu7XPCaLklDUZr89E8WkYjRA1B8Nxpx6q48W+4Zzgb6VkADgDp9EenStNLitvr8/Gv9XBoh4quKshUZ3yS6QXUpYUzfKj6+7Aey69HwYWI1mL5bkuluc6WJ7tYEW+i5V5YDhnUCj06641mcMJSCG1ogkrDZE46fxkn3o6nMR+pp2eAMu3dMLrebwX76GeJ49TOkE9BfW0GZYC0bJAjQD4HfsDQKQ215j+hlmc+GwPjRc/I5jyeZUu0TawnepVEhho6Jig8yszfI8f6E8an6dWsOdx2k69Ntvsh8W+oVIjxHtpMk2rqvjM1LG4yc12KX3Az3SbXX1WPpt6lNQroL8ClKCp+kJHhXkDrQvXaMkHQc4bAjD/poHTNnIeaYSmiWbe0480/NyLGkPqC6+lBk4rpVQfNF8BxG9MRwdD9ZOUpE9nasShkawxJja3RqpJDRqNBB0OTXjvtdde2B7ZbWiWPdoVvDFzPzYHJWw0RWxu5vGHqSxatq9Y2aAH8tkGVhYs9igarMh3MRy0kAmmbwdL4FAQpaJoSZN6lqr0+mJphq9BEFZgDA8PO2+ME5EeS6fTiXDTGl4CUYXSOnCdEGoQOAGUyqAScQEHECo56+oVMAksBPd2u+1Agi/R9j1OTnLtP/7NyaTGhuAyMTER6TP1EIE+uHLCsD/Ue1V+ll69UmZqyAkWQL/EjaDg00QKsGyDUmX0Ghkt6flqhHW8tT+MMRgYGIgYBlItpJIIEloGyz5Q4GM9ea1Wi2w1zL4tlUoolUouKtAteBkZ+KtNfU9XgbTdbmNsbMz1twIexyEIggjFwX4kvaRzT40deXxto+ZzOAY0/r4xV2NGvaae0hHhKybV6KZSqcgeSuxv3kuTs1qFRL3iOX55JNuizgifYzFe5gwsMTBftfFRvOJH30Irm0UjlYLNZtHO5TA+sBzrh/bAxvIKbMgvw6ZWEQ8287hlPAMLAn0eQxmLFfkuVuQ6GM12MZppYVmmjQHTgjH9Cg8mhJrNpqMgNBHHQaa3xElJSoTAyQQLK1h84GS5FxVHQzylIoB+zbV6iOqdqadALl8pGAIRQdNvKxBNiPKHy7ppBNgHCphAH4CVu6Rh05BUw3DdLoD9qpl+NbaaFyCgarsBRIwwr6ncrNZP8/psk3rpCmAKgur9+ZQZz+f/vLcuvtJSU6V8uLcP9Y3jxmf1vTv2Ic8nYFUqFTSbTefl07Nln7B0lt46N3VjVQn1g4ZRx1FLFPnWJRo17pnC5/IT/Ky8abVazqtln/jgqhEx9UnHSSMqTeISjDl+FD/SIrBaG31PgeqO5oU41zKZjKOGVMf9ZDDQj6jUAPFv0lOLIUuKZtnw2GO48Ze/RHfTJtiNG9GpVtHevBmdWg2YmEB3chJdAEil0Eml0ExlsKE4ik3lFdhcXI7NuSFsTg9ioy2gjn6HZozFaN5iNNPG8mwHyzItLM91MRw0kDUd56lSKRR4aXH5DlAqFY+nZ0dPxk9WAtP3i9Hv1BPTMI4gT2OiNI96KgRHhvdh9/TLuMjbkkvU6ENDe/VYCUD0mn0aS71zIPo+Rz9p5CczFRR4Dq+lHqv2PftbPWM1Mmy3cv5xvDtBi/2tnpiCu05+3kOBSO/NcdDabE2IsV95jK4j4PfW9hP1HCPfa+Yz6/U4Rlr2xzHQyJKeNPVbk4CqD6SHdHEYDZtuKaH9yjbSKHLc6BgoHap8PZ+VbaRTos/HcVJw9fMnahhorGhQdHzUo9ekpU+X+Lqieq3G3jcazD9YazEwMIBnPetZcwNejMxGsywpMN+4cSNuuukmN2ia9HCb+NTrSFersOPjSHc6sFu2IDU5iXathmBiAuh20crlMJEbwGO5YWwqLMPm/DJszQ5hU3oAW4Iiuuh7q+V0F8tzXSxLt7As3cJIuoUVuQ6W5SyymbQL3SgcfHpEWquqb9sBorytcrHK+6pnqxxgu92etomXcp+8hiZ6CAJsnwKF7/UyJKRysrxMr6+hKo0VJ45uHeBzl+qNqbephlCjEnpy7DulotiP7C/9TCeZ7+Vp23m8GhA1XgR25d/ZTo6depaku3gfggTBVekBza34uqKAppEdwYffKe2n48PnV6pIXxyifaHbCyiXrUliHVO2idEAdcfnvhXkFNjIVWuuQHn1brfr9IrfayKafUad0XyBAq8aRB1nGivfKMQlOlnQwOStzhXqJ5+X/aqUpOoTAKxYsQKHHnrotoFfT3YbzpxhDYXK0+mEW942m020CwU0SyWYlSvRRm/b3N5ANlotmGoVdnISxVoN+zWbWDO2Faa2DnZTBdnJSQSNJiZSZawvr8BjxVE8VlqBTfkR3JUdQiUYdPcOYDGSaff4+Q5WFiz2LKew90Aao/loglIHkpNQLb9OYgWCuOQqFZfe1cTExLQl7VqSpiVR/N6viKE3ynZpdQwQDT2BvsfF3+l02r3uTr1zrdQhMHEiEAjV+/E9K/YJoxz1xPmdAj2fiyDHMeB9/coh5XTduPZAS8GY7dQksZ9A1ASrvze+fq4RiF/hRDpOaQ16qlrJxGux/Wwnx1I9VdU9jRI1T6F5Iz4XvWmlQPTlLGocALjcEADHC3Mxk+oPDYdGUlNTUwiCILJtNY1UvV5336vx1QiLfcRn0Qo2Jm3Zduq9Vo1xPLQvOQ/U0Grkp2OozoTqE59ZnaN0Oo2RkZG5wW47ZEmBebsdblalIbdydAQrAJGQkJM9CAIE6TQyK1dG+CwAIUXSaKA5OYlgagpParex/9gYUL8HrfE6Os0m6m1gU3oAGzND2JgbwebsEDZlh3BvuoyO6dM2BdPG8nQby4vA8lwXq3q/B00DKRN9L6VSIloCSa5RFYNtBfocqC7hZthLLprn0ctQvk6z8Ew8Af0Jz3ebqhet1Il6u3wOAgefQ2ubNYxXcNI+0IVBagg1vCUgsw0K1EDf0CilwvNarZY7Rz1b3oMgpu9a5TXZJj4f+5C5B76wgf2sgEUw1CQspdvtOn6corkHHqPPzuuocdZrx1Ev1JF6ve5WPmseRSlAeq35fD4C6qRWjDFON0mxlMtlDA8PR6gjcszGhElfJmf9XATvwW0LfEqJRox6qHkbjoeuqGabeEyn018RqpEadY7jxTp05fI1wcq5QkdJDQAjZJ0XSgkyKqYRWQxZUjTLPffcg+uuuy5SJaCeKJWSHUjF5gudNYTTiU6FYV9wMNXydjodtJpNdOp12KkpmHYbnWoVtl5Ht1bHRCeNrd08tmQGMZYewJbMEDZnBlFJ99/CbazFUNDAiKljWaaNkXyYkF2WbqGU6iKfz7nQkhGHblzFNlPJCaQ6+RXY/HJCfqbX0uvRKOqEodHQhUfqQaunokqsQMljfd5dtwVWT1YnLhBNOPE7jV4IqGwrn8/PJ6i3SgpADSvv5dM52oc6gWmgNMz3wZHgrrSGH2HQ8HF8eG0aduXP/UhPqQzSBvoiZh6vERrvEWccOX4cH84Lnw7iPcnHE0B5XeXW+dzc6oKrm9kXNCL+PTSHA8D1EYGYu0hqHkmNuLZF+18pLjV0SnVpstxPYvrUjDo07EOODb1/NYgjIyN4wQvcRrPbJLsNzbJ8+XLssccesNZGVnMxbKK3zgHJZMI3sIyOjiKdTmNqasolKf3lyAyH6OGoEaCClwcGkBoejniRnCjLajXs1WgAlQowNoYuJmHHx1GpWmxuZbDZ5rHFFDGWKmNrZhAPtYfRbqSB8fDZsraNEVR7vPwUVuaBVcNZrCr1N5rSsB7oh3VUNhoADTEpnAj+Zlk+iM2UqNNVr7oXuyquTga9B4/lhOIkn5iYwNatW91xLJMjkOj+MGwjr8GKDC6Q2bJlC4DoTpa6UIzgxvFme5VXVu/YN3jqnasRU66U/ad9zyog32unXqnR0OdTzlmTdppQVKAmzaGGm8CjuQC2g+f6VA8BWSMG9g3HhN40K2h4r4mJCQe01AOl8vg6RFbPsI/8yEMjSQVw6jwAx7vTiOn2Huw36j3boR64GkeNehWUNVGqdCd1Rw2HGi72DdB/eTfHCUAkwl5IWVKe+bp163DDDTdEwq8gCCJegA4yEN3HgxwflZoDpOGjhl/0VHXDLFU25RIBuP0eNHyk9e50OrCdDjq1GrqtFmCBymQDE50cxltpTHSy2GxK2FRYhvHsQOS5hzsVrLBVrMh2sSLdxLJygGXlNIYHc0gFfW9RAZhcNieo8sJKfVBB+aw+N6n6wf5WL0XrfnlvTeQyOcYx4LEaSVHJGcLyWI6f5gMUXNg2JqZ0kqr3r3QA264AR+AhIGtEoXkajYTUa6ehUG9a+4zRlhon9o9WReg19flohDg+FKXB6MQobaGvKtPIh8bMby/1hW0lOHJMdNzU6+R9OH50LFj6qiDLBCafTXdO1OfUqEQdA62gYnUMx5fPyMiBY6aRq46xzgOtVtOSS127oP2tTqTW5lP3teyWekg9WLVqFZ75zGfGINzcstt45upRAdOXpZNb88vGFOQ4oAQv9XL0RwGIg6l1wn7Yrt4QEH2XIf+nkaHlLrdaWCb0TzroINteB0zdh8l2BhsmO9jcSmMjCtgUlHC9GUajmwO2ANgCZDstrGhNYLmtYHnQwPICsFeug71Gi8iNDqOdzQJioGjIFEQ4UQkU6u1xNSgnF5+fHiCASAJKjQcNiL/QSPtBjadP2+hE8L0tvU4mk8HQ0FCEl2y1Wq5eW8eGBr9arUZCdfV6tQ3UMT0/ju7RKgylkjqdjltLMDU1FYk4eN/BwcFIQtqvZlLw1HFTPl69S93TRrlk/q/AqIlDzgMuXuJiOIIbjYXvKZM20T4nWOvbhjRRzeiM/eNvJcuIQUsj+eyVSsXRh2wHE8ucb+r5+slxfUEG+1grZjRJT1EKkwaJpcj6AhulzTSXk06nMTAwEDEKiyFLCsx9L4QKRAXRcF7DeuUEq9VqZAKSW1UvQKtLNPlBgFGFVk+H5xIcgL43qwaH11KOcGJiIqSOggDd4TSyxmDNnmns57y1FrqNR1GrtTE+1cUWm8PmtsFmZPFwahg3Z8qw3QCoAXgYGLpvEnvW1mOlrWJZqonRdAujg2msKqZQXrkCxZUrYQcGYILoa6+YxefE8blg5SPpnbEahqGntdYBFvd5ZwJSf5SOUa6ZE1f7Xz1gpXS44RonLIGjWCw6Y628slIMpNKAfuJXeVr9Uc6czx6OS3T3xVwu555NDSXQf+uV7+0SSPhCB+0DRjfsI80RsP8JKExwEnQIiIwc2Xdsny6aYfsLhUKkj2u1WqRiSnMy/Jtt43tpeV2OM4+lsVHHghUjNLC6BxFzYQBcgp7Pxf4PggClUsmNp1Ic/J59r33Olc26zbRSWBrdKE3CuQL0o1ZddKVFGZq327x5s6M/EzBHv/6XIZHysUCfF6bQO1KPmp1MBbfWRhZqKMennjcpE/UggP5bjZSy8b03NQ5UJIZcvObQ0JCbuKQN1GgEQQCTzaCYzSA/0MVKd70uUqkqEDQx2TDYMN4MPXqTxebUIK43e6KS6q2WbAOpsTaWrx/DHvX7sEd1C/awFeyVaWPvcgrLVgwjPTSE1sgI6suWYVx4aXrx9MRKpVIkCctJRK+LhhaAC9l5LgFG+1YjKvUw6V1S1JPnhOZ9dadJrdZh+zgJCfgKRvQolbPW47RSSj0vn/fWaEI5WeVYNRdhrXUT3U/KksIjWCodpTQjASKTyaBcLru2Ki9Mx4XtUypiJgPFPtTIjPeip60GT6Mp0lk8hjrBeUTDS91QHl6BlW0gZ0/qg9djP1InOA9ppChKmXJcq9Wqy7X4nrjSi+xHcuLdbv9tYhpBELyNMdMWl6lTou1aSFlSYK5KrSGNTgC1iKqcSp34E1GBl4rGCaiWmQqt1per0/xJTw/CWusSYJzE9J70fYsEOSoGl2X7CqFeIJ+91WrBtpsoBhb/Z3kKT1nFNx61YG0VdZvGBAoYawTYPNHGhnQKjxb3wa0j+6Nj+oapNFHDqg1bsOfkQ9i78nvs3RjDylwHKwazqA4NojEyglY2i7FiEVPDw0j3vG/l0XVVHMGTYTCfQUFIq4X4fCwh5I8CKUU5e+VAlWIA+t4T98OgXhBU2J+MJKgz/vWU5lG+Wa9JHeX5qofaxjgPF+iDJnVSDZlGgfpDfdbIQu+vbdIIg8do0tpPyrKNNChKAan3y2sTWPncTIRqToHH8j7c+ZHgrs6XUhZaScLz+QxxVCff3jTNIRLKjMabfa9j4tNe2se6rbNG6tRTGu50Ou0iU/Zno9Fw23IvtCwpMGd1CgdypuyxKqHu4cEBpNeo1Aw9AA378/m82wZVS5h4Dd6Lyh+XqOG5LBnjBPJD+ampKacsDDfVgmtozvsx5It7US4VxlqLXKuFUruKvbMBsqPc3xxA0MKmmsVjVYv1VYtNVWBTdRg3DazAtehXwgS2i5XVLVj14GasbGzFssajGOlWMIwamuUs0sUiTKGAzNAQgl7FjxkcRKbnXXPsfMorzpByspFmUWOtFIhOTuVcFeB4HY4dMH1rAY2+1ODwt1Y5KFevNcx6HwUaIPrWGR/cFSx8fptj54Mg9YkAo22Ymppyn1OPVC/4HGoMqJMK5lqnzrmi0agaFnqmXJ7PNpOi0HayekVpC0aySiPRSdI9cQiOSvXxR+cd28f5RiDV+as0rOZV2Cb2FeehRj/adtVZRqS8Nw0JcUYNuurHQsqSAvNisYgVK1ZMoz98LothHfk+9QQUTKgIACLKBkTfVsP/CbBavqiTUydyp9NxZYBq7XX/DeXe+BzVajWy94tyvbwG76cTzE+4+nmEVquFqakpZ4QIMIPGYHQ4g4NWZBxwdbtdbK1UsH6qiw11g83NFDa3hrCxNozbGwFa6N+30G1iRWMMKyY3Y9XGLVheW4c9KpuwvDWJfDYFOzCAYGgI3aEhdMplYGgIwdAQzLJlSEkU41fP0Fixn/zx0/Hhc7IPtN91gqvXpyDp5zU0EtMXR+gYq5eclQhFqTXei14ejZFWlWjZKb1J/VHaAehTShqdUhRM6S3yntpvCpBsF/MMCk7aT3weNSTaR8yvKIfOfdrJcfN8Po9y01rhQj3W8VCj5VNemuug/nY64SZxNHDUI43Y9Fl5riY6deUm+0PpLh6nSVbl3+lsMGqljixbtgyLIQsG5saYZQCeDeAGa+2mhbquSrPZxNatWwEgVpmB6CIBhmQ8XrlGDpSGmkyGUeGUniGI+zyqVgVoVQPboMrBhE+vv9zAk8Pn28DZXgU4DQMJPkoTWNt/36V6djpBtBadEQYpHXq25POHizkMF4H/40LeMArJ5vKY7BhsrAd4ZLKNTY0c1ldW4v7qClzf7FMhxloM2xpWNcaxsr4FKx7ZgFVT92NVcwyDrSm0gwCmVELQ+8mMjiKVzyO1ciXMsmUwy5YhkL1hfGrDL1vTSAlAhNvVaptSqRTJs6h3zHEhwPJz9hfBRHWN3uXY2JgbD58K4+TXEkA6D+xzTaDy2hSCr0Yw1AlSWFp3rtUv6mDwflrVpF456S3NSagToZSYFg0oFci+15K+OJpKKRTu0cKyPnVCNFpTx01zVfqM/EwNkV8mSPHHl22kvnA+UahDOle0jFkNCqNz6if7SPthoWVeYG6M+RqApwO40lp7Zsz3ewL4LwA/APBPxpgXW2s3LmhLES6537hxY6RuUyc7+Svl2xiCK5D5q9MY6rHDNbGniSYNA4GoZ01vVwdQQ0BNjLBSRI0G7+uHouo56ARQz0Iz/bwWlZmfE7A1VNUFRKQheC7vwTCcE7hRr6GYTuP/lNJ4+kh0UUU3yODRyRYenWzjkYkOHp3KY0OtgN/U90Rj5EA3jll0sAI1rGqOY0VtC5ZXN2HFnQ9jeX0rcraNoN1GuttFqlBAplxGq1yGGRlBZ2AAZmQEdnQUGB6OTFr1gDnOnHA0jKyaUA7Wp+I4nt1uN1InzfFgNQqpNgUDAhonq5b2qbfrV6lojoCgxP7XhDt5az6vJlXVmdC+YLs0UlAaku3UvmR7FMx9AFfRaIjtYhJfcyPKSVMv6bHTWGjEzfbyufmszDfpXOS4sR85F/leAb0Xr88+5jjxGhS9lo6Nfu5TqkA/qvQXaHE+LtaioTnB3BjzBgApa+3hxphzjTFPttbe4x12IIATrbW/NsaMAHgWgP9e6Mbm83lX9aGTQkMaKhYnEdBXRK4a9cM93RMEiH/XIz9XbzCbzU7zTtTDU6BWq89Qi8qvi4xYasmQGehzpWqkgKinoF4HV/FRWfmcWqanoT7P8QGAnhwTs/Tg+JZ7pQ7CCdHFvuUUnjSYA/buvz6u0+lgrAFsagTYUAfWVwM8Vk3jwUoZN2T3hh0CsGfYx0NBCytQw4rWBFbVt2LV1AbssWUdVt19D4JUgKDVAqyFDQJki0UEg4Owg4NIrVoFWyyiMzqKzugobLmMbi80Z5ivhp+gXqlUHCioh6yTUXlU6gH1i9GUAqjywfV6PZLo5JbDvJ5WYFEXOUZqRHk95XiBvsHwt0bgtTmeWvlBkKSuKW1CUR2jaESj1Jgf/fKHG29xHvjluT7FpoUFrIRhfzMioAPGMeIc1QiM84HRNt+pGgfUxAjqhT4vj2GfMnJiW4gn3JiO4ufR/Ahpp4E5gKMAXNz7+2oARwKIgLm19icAYIx5AYBDAZzhX8QYsxbAWgBYvXr1djVWvUZaa3oRACIWtNFouAUGyr1yImhCTZdeE0Q1vKQiqEJo/azyghxk5fBV6dvtdqRsjkldfscMOz0RDcno6ZA/VKNGRdb3GPIenPCamOLEY5JKPX3eX3eU0/2tU6mwZlzfL0pRr5eT11qLAprYK93BXmUgM5Lpj1Org02NABvrwMZGChtqKWyoDeCm2gCqqX2AEoBVQPppFsszHawyNezZmcKetS3Ya/IxrNz8MAb++EeYm29GrtNB0G4D3S5MKoXuyAi6o6Nojo6iPTqKbqmE5sqVwPAw7OCgAwM+h1JqPb2e9nzqJeuE9T039gWNJnVvcnIy4h2rl0eAVZ6fQEAvnmOgXrACil6b4KhOAMdYKQaNDAFEtkDgs9F46VYOpHj8vBXnhC79p07VarWIQ8Ln1zmpDhHb5DtX+uy8VyaTidS2q1es9JfSl4xYNQoiWAP9ognVB6WceD2tfdd+VoeK2OVHggsl8wHzEoBHen9PANg/7iAT9vJbALQAdPzvrbXnAzgfCJfzb09jJyYmsG7dOhcaaZ0nKQx2JGkPUi1+3bFf7qcJNB7DMiv1njgpGR77NdJaYqUeMYUTRJVaS574ijYqGtuoiRjlCdVb09WNnIDKF/P6xWLR0QT0EmhItK1sX6fTcduQajTAWnA1LLqpkFJcSk1pPXg+n8fqfApPGo7W+aZSKUy1gI2NAOumulhftVg3BayrlXFLp4xuZk9g2YHAMqCcsViZs1gZ1LGyPYmV7QnsPbkBK8fWIz0+huDee4Fbb0UKgEmlAGvRzWQQlMvoDA8jOzSE9ooVSBUK6A4PI53Phzz+wAAgwBonmmfQN0WpbimocLzYzxy7uMSg0hwcSzXcQdCvtlCKUa+ndAJpCvUMlYZR7zIuAe8beG0/dUVpIXVi+Fz84bnUYeWyNZIC+o4ToyylC5n7mZiYiDhhbDejSm4bwD6knvl0DhDd8pk67/Py6uwxklIdYV/6FBpzG4sh8wHzKQB8aV0ZQKxW2/BJ/8YY83EArwZw0YK0UETfachVZgRFKjcHmu+55GAqZ+ev9OOKR1p6Kis5U64wo3CyqNWnt8tz1SPSPTJ4Ho/htfwl1ARLXWhBj0C9FFI9ujtdbzwiAAH06+t1SbmGgvye9Ak9NL2/Lv7wX3On2fy4PlKjqn3R6fRfgaZJrZGMxXDBYv+hqFfWbLXxWKWDjfUA66sWj9UMNtSA26pF/LpZBLAKyD0ZwR4Wo0+yWFkAlqdb2NPUsaoxhlWNrRgc24jM1q3IjI0h/eCDMJUKYAxg+nuioNtFkM0CuRxQKITJ2mwWplBAUCwiKBbRLpUQ5HJo5/OoFApAPg+by8EUCuhks0A+D0jOBOiDPIHQ51eV++fqUPYtDbo6JwRNXY5OJ0J5fd5bx0n5dl2d6+eRNP/ie+A0ZLw39Zp6wyg6n89Hkor8To0ejZ3mwZjs1P7RsmA+C4FT5x+jT7aFzgh/a0TNcdcImE6bOmi+t63jqHNIE6t8XrZzMWQ+YH49Qmrl1wAOAnCXf4Ax5u8ArLPWfgvAMICxhWtiX9hhnPya+Scw8/9qtep2fdOEiGajNaFCq02A09BMEzy+ogDR7DyVhy9xpjFQj5oTWb15oL+4RjPwbDMwfftaNU58v6PSP+wznyrSpdaa1KXx85dE8xk5iZTLZRvZJ3xm9qfyyDyP3qGWeWk4y2tqHoDGkH2wLBNgJG3x5FLfo0qn02ghjY2NAI9VgfXVLh6d7GB91eKusSzaNgdgCMCTUChZrBi1WJm32KNksGe2g306U9hnaiPS1SnYbhfdWg3teh22Xoep19FtNmGqVWBsDCn+z4lJXUin0QkCdI2BsRYIAiCbhclkkMpmYWkMCgUE2SzyuZzbVqGbz6Obz4e1+sUikMmglcmgk46+SYoAo1QaaQ+Ogyam+bdy+QRUjheFgEPd0RdqKOCpE0Cqip9TH/wkvhqnTCYT0Q3qGJ0T35CwzXF5DBoxLv9XEPb1Tp0Ha63bXoARDu+r0SOfixSsVt2o8DvtJ/Xo+b+uaF5ImQ+YXwbg58aYvQAcA+BYY8yZ1tpT5ZjzAVxsjDkewK0ArlrwliK6a516qQRaAiSVTEM/fsZQUSsN1MsBoqu/CJDq1arHoF4/gYm7rql1BvqlafSa0um0AzEOsBoY5bQ1YaleBifJ0NBQZEc5einqKTAkZVSjCgdEX5HG59SyOoIs+1Ynoio7hc+g1JLyrco/sw/j6CIaYq2/5719SgEAlqdSWDkQ4DnLc8jlwr29LYDNNYuHJ1pYV+nisUq4UOreqS5+txkIA84RGAwjZQwCAwQpwJSBoAwExsKY8Kjob4vAWgS2C9P7HcAi6HZ6v7sIOp3w/24HqU4bqU4bQauFoNFBMNVEsKl3vmkhsA0EGAdg3T3CPjIwKQMThC9YMameQc+kYdJpINObyikDk04jyKRhUimksmmA/K4BYLuA7SIVBIDtIjBwf5ve689ttwNjgHQqQMoECEwA2+0iMCFFlU4FMBZhHwFIdQN0O+3e9S1S3TY6nTYCXgvhPdrttuO1OefoqavzoLtaKr9NQ6Y//I5UKw2HeuGcN5yj1BVuKqYGQJ016q8/Z2k4GHEQW7QogBjB66jXPjjYf2PZQsq8tsA1YYXKSwFcY61dv6M33d4tcCcnJ3Hfffc58OMmPewwetA+R6uW3E+k+MlTTRhqRYFyaxo6Ugk1aaVGgt6nehbKM1KBlL8DosuxgX7ZIdCvYefzabhJHlUpDX8lHD3zycnJSDitHowmhpUTBaLem7ZBE0N6LPuGz679rBNBQ181vhQf1HVTJqUrlAPmc9D4aoKL7ax3LB4Zb+GhsSYeHm+iaQHbgzZrgY61sNb0fgNdAN2uRadr0bVA14a/w3PQ+6z/Of/uWITny+cW4Wf83HZ7pZHWotvt3Yv3hHE/VnRjqQiNBY2gQc8gGDGQPAY2/Lx3DP8PaGjlO9vt9Y61PaPUPz4VmMg1QgNmvPvZnsExSAUG6VSAdCqFVGAi97PdDmC7sLYbRmJyL9gu0ukU0kHQu06AdDqFVK+dvG4qCLB65TDecswLt68Pd3QLXGvtVvQrWnaaMERhuDbce1GEJgEJAvQilUdUMFV+UOkD0gM0DprgCoIgksAiYOmCFUYBCkJxvKbPu9EbpgerIaICOw0AwYseg3KkrOIB+rwkvQlNimoJGO9Fj5xAqt44EM0X6Liwnf7CHYK0X+sPRN9JyWuTXuL5jif3tm5oNptu1SFDZe5eR2PDKIx90Wq1Ih6cGx8AqwcMnjSYR7ebnZbz4A8jQj/64LX0O/6tCTIdS/3NZ/evq96n9rGj9CzQ6fYMTL0O22yiU2/ANFvo1hvo1OvoNtvoAuhUKmi12rDNFrqt3lxptmDbHaDVCo8zJuT3LdANgp4xC+miLgyQyaCdzqCTzaGTzaKVzfVooAyaJkAnX0A3CGBsz9ikM+gGAbqpNGyQgg0CtHsGDAjQtRYwQR9OgwDdLp+r2zN6oYE0QSqkrHrHOiPXtegag3bHoIO+oYQxoTHsALZ3jS7kbxrS3rX0816cOgcabb8cWengLYtw3SW1nB+I7gtOYPIpBt2Yn567euVA9HVjCuisoyZg+ck9BV0CGl9ZplyyerLqPXOSklbREJOUiV/Pq4CtXi+/p6Hg/uPM2PtvVapWqy7xCURfXqx8O59F76GJKoIarwtE997W5Cafm5uRqUc+NTWFTCbjdrLjuzQ5hhoCs72kpjSxOjExEdELJrhpOHWJvr5sWY9R8FQQ5hj548pIxKeb9Bz2i+YslCKIMwQ8BkCsQdYxSAFIMYeSH3Bj5pcl8vqcF7w+KQHT4/eDRgOo1WAaDbQbDdhqFZBS2e7kJNqdDrqtCmxrDJ1qG+lGA7bRQKbRQKrRQKbdRiedhjUhoNsggM1m0bU29JzTadh0GjaXQzebRTuXQzuTgU2n0c3lgGIRaQBZAEE+jyCbRSqXQ1AuI5XPw2SzSPd0Rh04P+LW6NXPgXGu0RnSvAGP63RoTELjYIIUOt0wB2JM0IuMArQ7HVgTwAQB0ukMUr0fEwTOUNEI8f+9V+y80sRdRlilQqWn58X9V4A+XaJeui7m0fCa/wNw3rG+udwYE1kuDYSK4VedELDUA1bg9rlpfkaPXp9FX/2lCSyeHwcKvCavx+dYtWqVS05p0pPH0CtXw0jP1+fp+Wy6dJkTQFdDqseugKbRiiZAuR7AmHBVH+kTXaikiTZ64ppPIBCqB673JfWmfa9tUVDXSc97qlHiM3E8fOpJk/B6jO+N+99rAl9rwPWa1AmK/6IFrVrRdhDcOT84NtRBzRFZY2AKBZhiEUb2EHE8MoCM6Jwamk6jgVathlS7jVS1CtNowNbryLTboXddraLVe3E6Oh3UOh2YWg3ZsTGg04FpNBA0myi2Wsi0Wgg6HZhOB91MBo1sFs1cDo1UCjafR5AKKY2iMchmMsikUjC5HLqFAlpDQ2gA6GYyaGezaKXTQCoFm0rBZjIIRO+pQ1pRpP3GPksHQKmQi9C4fi4s7AcgnbYIgvAHsAiCaES9srA4nPmSAnPldIH+vtfq/RCMOGHilvwC0aQfEH1NmU7AUqnkwIdeO++hr5PjPciXa62x702TuhgeHnbGh1FEqxVuiMWcgHL9PDeVSrl6XQVWLl3nuZOTkygWixgYGEC5XI682JrGi3kG0h1UbAI0vRZNGhH8yM8PDISvudMogOfpboVsOwFKFxVpu7nZGMeXY0iQ5TNzbLjQimOn+QVj+nutUzTyoXHjPQmq6omrqHHVmm/2pxpyjRbYdj9xq7SR6i37S8s1VX86nY5biavPzoiINKAaVwVfXW3pJ/w0MtG8jh5LJ4bfNZtNwBi0s1m0s1nY3pbDapDUEEbyPKn+C5JhLVqNBhqVSvjS9HodQbMJdDphTmFyErbdhm000Gm3UW230Wg2YaamENTrSNdqyLZaKHS76KZCaqZlQyqmns2inc+H1UbZLLrFIlLGIBMEyKXT6KbTaGYysEEAUy6jm0q5OmxWG02l08jl80j1dorMZDJux0jqk0b0dBDoELIIYTFkSYE5O8cPqYB+coxvYNFzaATUo1ePSr0lPVa9eXqPqVQK5XJ52uShN9XtdjE5OenAld4sAZz3r1ar7mXGNAAEsNHRUQBwnjS9htnKDDXJSwXibnZbtmxBqVSKvB6NSkVem8aBNAxLHXWRBw2a1tSz/FNr1wFEwIBt1IQxv+dkIIjy+fgKMt3SmOCjoKKRCQ2rRgQ6vow8tOxMPWJGYeptKpBpZYMaWA3RSWPwfsr7qy7yb4J1q9VyJaxxHryCvxoDpehUdG9zrfrQ67KvtUpEy0x1fsU9q4K/vgpPoxTuhqj6oFSR6oJ7xkwmXKXLZ7f9l40Xekl5a/tlgE1JnreMQbvVgmm3EdTrsLUa2pUKWs0mWgBMpwNMTCDb7SJoNhHU6wh61FLQbCJdryPTaoWrHlMpNNNpdFKpkPsHQu/eGKQgxjGVQpDLIZVOIygU0Mxm0RocRCuVQhtAkE4jlc3C5HIICgXY3rbaCy1LCsyB/qqtSCJIlEQnvO9l83utT9fJoUlTfk+PSZNVOlGVAlEvi16sek5UOCq+1mHTa9YFIpowVD5eQ0GlaFiO2O12MTAw4D6vVCqu7l6pC38S87l13xFy2fRCucc7S8oI9NVq1XHfbDe9y3K57O6pIE0jyTJEGoRsNovly5e78dHtBxTcdfELDYOGs0r7aKSm469elJ/X4HVUNNJi3/vJah6nxoXHKj2l3puWmfqUjnK76mgYY9xLN9gX1G9GhzQmnAuMQhRcgeh2zhrZcD75nL46BcwB6Ta2Gj1rpMLPtB+UDtXcDcdaCwgY0eXzeaxYsQJA/0Xq/Gl1u+hYG3L35TKCwcFIVAfOp3AQkBZnK5vNIpfJoDM5iU69jnS1ilalEm4TUavBtttoW4v2+DjanQ4arRY6rRbQbgOdDoLNm0NqqNlEpt0OSzJ7OQR0uwi6XbRHR4HXvnY70G92WVJgHheiqMJqtYHvcQBR74bnEqCA/gt/1eOmt0+F0sQqlV4NiHqnDD81bOdkZJ01vWAFBq1nZWiuyq5gxcmlix44OfjSgIGBgYh3zGiA3jt3EywWiy4hSv5e6+jZFgIi6+9Z9056Rvl8TmRNRvJ8BSmdkBwbbj1QKpXc/fh2d6VvdPWh71HyWkD07ThKYVAv1PskPeN7pwynCWQEFgU/DauVmyc4q0eq7aF++9y5giv1TKM5TWhzHKhvWulFUZoJ6L86Ufcb4Xzwl7urA8Jn8Pl3jS5IpenLMmi4OIb+cnp+T9pPK78Y/XCPG/aNtkHXn/B6mvvQBDR1ko6LtRbWGGRGRlCQ9Sfsx3a7jbRERY1GA0Gnv/rTVW4FAdLtNlKdDkyrBVOtwvSihGV77onFkCUF5ul02vHMuiycQKjJTKDPf+vKSfWudf9vHWRN/PF8Xov7myg3qZ4E/6anqeEvlUeTjJOTk075WOWhITgnLD0wDS+VBmq32w4gCdI0fPS6eV3eO50OX69FEOEPt0JQYNBNiPgZ28ftDrS+m20kCLGKRI0UgYEepnqGnU4HY2Njrn/y+TyKxSIKhYJbdEG+m6DA/mOf60QnKCjFodUdHGuOjxpKjh3vSZ3gOOsWEErpaDJXaSKNHkhXaQUW26vJZuY0SPGpbrKvddM037Onnms1kHrLNOD6zOTGdUdNLcv16RLqmFJLvLYPyADc/NC9Yjh29OittS6BzxySrsVgZMDEpK7vUP0LgsC9mpGOha4BYSStkRIAF8Vpn6ijyHlJXdZEO49vdzrA6Kh7tvau/nKKx0OoKFRcLQnjBAKiVSP0WjWJpKDhe/KcXFzdqdyiluJpwpOK49es++V4pB6AvrL6ngw9BnoZBKo4hdXnUcoHCCdwpVJxXmwQBO4VeJrgJFDQQHHS1Ov1yNviCeasoNAkG/uL19AJonSYJiKVVuCkIjhoJEBjSRqnVqtF6v45vuxHTlL2JcfVD//VqCvlxMhFX9KrXqeORSoVloDqG6rUe1dw5/PxNWJKLeheHTRc6vHTSJGO03YTfHSvHeqNlrOqUfPpOj6z6pNSG0B/VfFM/cjj/UiGxkHbrxSYAid1QnMCGm1y3vAczWkoBaTPBPRXlBJwabT8HAifk8aPtCWfz4/4tM28n0bibC+jBJ6fvAMUiEx0TlwNF3Vw1EsjuACITACCq04Mdr4uBOJg6OIW3ovfawWLGgDdy0SVlufxeAU+fq57tvAZ6vV6xJtXA0CF5cQm78zXZ5HTZBJXw3j1BrUqhuEjq1gmJyddKKkrRNkfBHVu61uv113VBcFG9wZhu7knh9JNOib0+iuViqvrz+fzbtLTuNDj5HlaM8/+UXDw+5GGl8fzufyQXb1sDfPpZSv/S4DQSMfP81Anlc5icjkuyam8Oo2C6ruCuB+RaXI3CAKXgNYELse/Uqk4Z0D38VGKQq+l1AcNMa/rCx0fzQmxz3WOchwZndGI0qFjX2o/0FAQYI0xkciF99eIjPhCXFBdZXuow5yjqqv6Q73hfvlKbyq9tpCypMCcPCpFvSodVPWyNczyzyPQ0uP093fQagHlHDmx42gR5c91ojGMU8uvYK7hqhotUiE8Vr0bChWOCl6tVgGE28syzF+2bJnz1NkWTmAFEn+hldIsGpbzmXgeoxjls5mIy2QybusFBVL2se6fwb7w6QD2DTl+hrjk1Nn3GkGxPxhVsG91Mirnq6CqoTxFPUA1ur6Xrx4mJ68mY2lodK2ChuU81je25PA1WalerPLr+jwKUpoDYX9zzDXSVQOtTor2hQI5r6N160ppadSsz6ZUCR0iTZTyXu12GxMTExgfH3dt0ufjcXxBB6u06JTRuPKeumVEnB6o8aBxpT6qAfAjQR3PIAicvmrJ9M7caGuXEQ4oEFUsBQKK0ivqrem1eJ4uqNAkhtaoc3L6iqEgC0RDNuXv/GuwRl23B/ATpED05RdUHP7NCadVHQSwdrv/TlOCKitRyA3GlQlms1kMDAw4wNdVsY1GI1J+ppFNrVab5vHRcORyOQwODjoPWw0Kn1m9Ut1at1arRXIE+XzeGbOpqSlUKhVs3LjRPV+xWHRJZQV2TkLf82Z/azJW6TTqiAIxx12pIdU9XcykkZ+Oo4KoAooCkxoN34hoJKF0iOosAU2pCG2Xv+hKPW32j79OQ40SgIjRU0qI/a/304ooXt93BnSeqINGwFQHSecGv+fc5Es+9BnIb2v7eU0dQ3WUlA6kvpBWJejzOXRRnuoDx8IY46rWFkOWFJhzgqtXxM+pSJqp1u/Ui+ceHf6SZwARRfevTeXQF/6qtecPgWpiYsIpEwGGFl+TSkoFKYhqKMlQVyezetQ6IblFAa83NTWFyclJt0k/E2nq8RFYuLo1m81iZGQE1lpX2UIj1G63XeirPLrSRZwgTMay7DKXy2FkZCRSs64rVDVMJwVQLpcjnC3bzvZw8ukueAQNUjJAnw/Vkjf1xglmqlekHPi/RhY+YAPRygz13HgvngdEE/UaDfrcLP8nvUCvkG3hfRSweX3yxH74r44D26aGQftCIydGB0w8qwcORDeu47mMZLVPOC+UT1fvl/8r/aH3IHAT2HmePhfb4b8hSeeuRn9qJKlf9Xp92qI1Xp/HaITAH7aFTiPbrauRF1qWFJgD0fdzqietXKYffgH9skQgfMmF8nuasAAwbUJw0NQbUOPBz+g1UFkZ5lWrVfcmFFpzLdfSkB+AA/1Op4N8Ph/xLtV74ATltbTOWpWWbVAjUa/XnQfM0DCfzzsDQkNAMBoZGXHevu6LwUml4Sb7TRWdvDsXS3GCFYtFrFy5Et1u17VRoxSf7uA40Yhwq2E1cuTo6dXzWBoTArTvPWkCTx0G9QrV+BMsfW5dk5j+oiHqilIxOq5+xEmA0/FgW5S75jXVcAFw4Kv632g0Ii+50L5VepIvfiEwUUf5mU8vEcQ4L7SAgA4M805sk143lUq5uam5MTVySmloJKXFAmoY6Bwx8cwx0Fp3NaScQzRCfkSluKERnI6dlt/yGdVx00hnIWVeW+AutGzvFrjqVXEyKOjyB4gmQzWZpjW2fkjL6/hhGAfUv66GzzqpfGXn8ZyUfiKKyVXlILUtfoZd+0NpAW0Ln5OTmyCt/CjQrznWRUf0vPxj1PujF0+woJJq1l49bJ2IOk68Nssh/YhHPSEaDr/KRz06Jn9JAbG/9XuCHks5VWe07/1x1mSpeowaofgrIKkvanz5WVwCnkDEZ1T9VQOji4HUY1RKQ49X4NFSSR7nh/4cBz/Byrb7SVt1prTf/GN13LVkllSLH7X4zg7H348+9DjtZ+qAJmJpGFQ31RjpnFUqjg4Lox3eS/dtol7xGTUi4PnDw8P40z/9U2yPmB3dAndXEU5mIPqGFD/E40BxEviJFWC6B6SAzmv4nJ8Cvu/1A9EQm0BKJTDGRN7XqAk6KpmuACVQqIegddzdbtdVIfBZuaCGx2l4znpwKjc9bE6iZcuWwVrrvGMmIHkt7VdNENHYaGjMe3Jy0Egwo88JrOdOTEy4tmiOwR8vAia5/3a7HVmVyiQvuX0mzP2wmjtKcvKx3VrS5nuCqieaCKNB08Sx71yoB6gGSqkCTTry+hr98dl1UzDen+NNT5uATf1iv/v5G+YxSB3qD/tBjalSJNoPbLN6otQ/pbfUyGjf8hqcxzrHtA/98WE7lb7hcWyfv84inU5PMwi8Dj9TB1BzL3xOpdC4VkOpIY0GOY8VGxZDlhyYa00vMB1Ygei2spoc8Y/zQdz3zlV5gemvh2OblF7QNvi8u3p1nFxc5aYcG6kBDauB6JvM1VOnN8pl85pcJcCTSyYYZLNZxwmS2igUCli2bBlSqf5GXlp3rvSHvimICqv7slA4QUjxUKG11FO9PfYd/9ayPBoiHkvjTLAZHBzE6OgoarUaKpWK8zbZz7owiePDa+ZyOQwMDDgg8a+t4KRhvu8Nx9EP/JvPpaDH5+N9dUGLctkaUerKUuq4vwqWdflqgIF+wlLpEv6v1Vnqfer2E4w81COnI+Fz2PSIGU0DcGOgAKn0JB0PnXf06Nk/vpHTPuFx1PO4UkCNqJRK0ShNjZKOJceMfcG20HHTHJT/bBzjBMzRr1BR0XCYE5NhFDvYrwOdSRRwdSLx3vq93l+9d5+iIRhwYDWRx+uqcnJCKYhpKMjP2MZOp+P22tDkKvcFJ6hzTw4CNAGD9AY35apWq86L5/J+JiB5Lyq0UjcalfgrAFOplCshZFRAg0BvU70tejTKi9KIAH3KQAGNz5dOh3vUl0qlSDvVs6UekNYhEExOTrrKH9Z4s199A05DrTqlbfYrQKiH7Ef2E69P8NGw3gcW7QP1QvV70hBq7Eib8Rl8mk/vy+cA+m+oIoCrjvM+9Pz9kkSl2Oj181n4nS7m0chFQU8jAdJnfCZ1LrgylucxWtIIN27PJX87a5+20eorGjzOW926QjGCn2mORaOFxQLzJcWZExT5t/5o2KTlVD4Ax3noen3/t//ZTOKHhD4XCiAywTXZpB6eliBSATQ05Xe8hyZ7tA6WFIPunqfHqVeovK56fbpIQ7lTNUx+WMqko5/9B6KRhZ/Uo3eppV58hmKx6Aw178dJ5nPejCL8kjoFB902mWOn48DP/KhPDbof2qtHz77h+GuSk+cxGUjnhN6s7q+tjogmOfmsTO5pBZE+g9ITyluzbzV5S3qAlISW4ul1lYOmLmi/cCwJ1NoH2o+qCzyO46d5IAK/H0Xosnn1kPm/jo0fLbFPaIx0tbJSMUop8lz2g1aXqa5rf6uBZL90Oh0MDw/jWc961ixoMrPsNpw5B1stNjuXnLRSKjOB9vbKfMBelUUpGXoDahQYPfDZNERliEyvTY0Fn5dliMpNkxYhheIru1ItCszqMVhrXQUA90QvlUqOAiDQKOfPfmdZpD6vgh0NGDntbrfr2szdE3kt3Va3UCigXC47z5qGhh4+vdFSqRQp71SgYN+32+1p2wHzR70mpTsUEDX01rGlLuq4Uw80QUjPnu9g1ciGeqFjRaqAAEcgpd5oglw9W33nqRoSUnH8jOCrry1UPeMzk4qgXlL3yA9Tz5TGmokeUV3g5mIa4eiqXzUcbAejSzW6msz3uXg/mub3fHbl9XkPnsNIS40c+1CNP7/n3Oh0OpF8DO+3WMv5l5RnrpwpgY+d6O9NsquID/JUWgVRn2NWasbnRwn+nJRUOvLC7APduImgolSN7xEpb01FJZhzGwCuuOSSZuW4udKNbeR1eH8FE4IowUm5YFJB7AM+K8GmVCpFXuRMQ6feIoFQE8J8JgITPWNNuJLf9UGH7We7+SwafbEfFMg1/OZ4KSBrjbNWxVC32SdKzxCIqBtqMNgnyuPyef0IUR0inwJSTNDnUT1S+sPPGWnkovkUdRbYX0pF6P04V/zyS6V71FP2q7rUAPFa/K0GgICvuqCRFO+vFBc/p87QcWA7dUWv0l+M/pYvX47nP//5swPHDDKbZ76kwByI8l3a2UA0nFSrvKuLKqYChHpZ9HSUmuHE1+dX/o9eNJVcE1dKkWg7eB2e3+2GOztOTU2hWq06D5IRg5aYdbtdt2+6TnZNErKCgu3U5db0xjnJGQIT+IF+fTnbx4VQ9LSVRuI9FBw1mgD6G1uR7tAkoIKXemv+eOlE17CafamUmr8BlgK99pPShnpP9U4J4OT5tRaaToFGHDSk+kxsl+qbRip+5Obz26qvqgu6pzr7gkZDdY1jqtUnSpeqkeN5Gp1oVKLGQp9dnRg+H/tVS5zZTqUleY4ufuKYqhEiqDPSZLv989rtNgYHB3HIIYdsA0L0ZYdpFmPM1wA8HcCV1tozY74fAvDt3vWmALzFWrvgGxDQM2cyS8usVAHV+1Rg31XBne1SpfcBQz0gKisnvD/x6CnrBkUAIqBJ3liNAyd4ux0uh6ayMyHK1aQsayRwciIXi0UMDQ2hVCq5ahJSGsof0mMmEBWLRTcZdP8WLRtUz5t73LB8UveG1zcqsX0+MGuJZrvddouMlM7yPU4CkRoLBULVMS0JVE9caQu2UT1R9eyYNFRDxrawX/L5/LQ9XnQMmQSk4VVDzrZrTkMBUPWB91PqUs/XtnGctRpKHQel83hvpTxIC+riJ118xufUtQQK6EodUjRSUH7f59LVwOnnGh353rfSTIODg5G+VadLx4pbOC+0zAnmxpg3AEhZaw83xpxrjHmytfYe77C3Afgna+2PjTFfAvAKAN9b6MYq7+uHdb4HoEoZVyetg7qrCZ+H4oe6QH8fGf1OvRtOZnrS9BC0FJJKpt6wKiFpGu4TQy/WB1hO2kqlgiAIXBVEJpNxwKv3Zn08jRHrwgd7b4SZmJhwoSs9IHrfmrQi906wmJycxOTkJAqFgnsurQLRCiGl5mhcFDjYb5qQ1DJCn0agR69bHhAYyOXrqwGN6a+DABABVBpI1lSrl+t7vPzR5KRfvUOQ0nfK+slvBWVGvARWjRTVKHJTK56rdALnF8eHDohSUP6z6bxkv+ieLQq+yqFzTDSHBCACuDyXz62evBpsXpPjR0fKWutyMzTOGunoEn2lh+hY0Tkip7/33nvvGEjEyHw886MAXNz7+2oARwKIgLm19lz5dwWADf5FjDFrAawFgNWrV29HU0OhNz6bzATuCnQU9ah2ZXBXjhqYTs1QmQkKpD000cQSRg2X6cETgFmup56+UiFAn6/XxBEA57nTw+ZkYtkjvXeex+dqNpsYHx9HtVp1tMnQ0JDbhkD3n9ZFQLwXn4feZ6fTcffjMzHZytJI7iypIMIqHK1I4WROpVJuP3jqII9hwtnPbSj48vhWq+UiAZ+moBfuAx0Bk3+zX0lP6OvpgqC/OMj2kpnqqRKMSaGxHE/HnONCWsCPHDhX1FAq/01dZW5FvXF+rw6ZgiefQw2EXl/bSGNKGkOfBUDkhdf67DyGxkVXv2qU7Hvy+sJz1Xs6Pnqs0mF87WLcBlwLKfMB8xKAR3p/TwDYf6YDjTF/BmDEWvtr/ztr7fkAzgdCznzbm7r9MpenGwfuyr/vijIXNaOKzwnO43y+0VqLiYkJtwEXqyB0DxlWqdAzVg6Vk4jHkbrgvYvFotuHfWxszJUcEhS5HwfLCvkCCjUyGurScxwdHXX30moY0jqcQARBgqPuta50EHMMfoKMf3PS8/mZjAdCICHFo4ChOqZcuF9Jw3uoUaXeMiLSPIZGCgRW/qY3qN6lAjj7BoADGKU6+Ky+l04PF5jOedM79ekKtk/nk6+n6on7ukwd57NrocNM9+B5WsLrgzoARz9qVMPjdMsIxQCle1RP9LnZZkZ4SpMpbbbQMh8wnwJQ6P1dBhBLPBtjlgH4AoC/WJimLZ74nu5c4O5TM7uizGawlEIB4CgPAiN5aO6umMlkUC6XHS/JZyfFUiwWI3yohs7K+Wp4OTg4iJGREbcn9cTEhGsHqY1CoYCBgQEAcOVxlUrFeZo0SARf8vEsUWw2+y9TYLt0BeDk5KRrI8F92bJlzrMiPaLhtyYyNdkFwL0bNpPJRMJzTY6pLgGIvI5PKzU0kav6qaDBPlfQYU5AdVOpE7aDXDYTgAQfrQunzpDTVx7cj1qVmlAd4+cEUj+pqJGw9rPSTUr5aEknz/fvr1w/j1Enh9f16Vf2B3V8aGgIQH97bKWN1OOnjvB+1AetDmI76M3rfXYaZw7geoTUyq8BHATgLv8AY0wWIRXzEWvtHxe0hY+DzERj+APPY31qZlcUHxCAaC27Hz6TliF/PDY25jxF5UapsPQeaQhU8QkQg4ODKJVKbvtcABgdHcXw8DC2bt3qqmNIgbBqhqs41ZvkhGB7mSQjdUFjQxqG4K6hNiMOtrdSqTgg1WfS+2jllFIJWmGhr4JTzpfX1MlPANAtBsrlsutfetG8PyMh5i/4OauECoVCxIjzHroKVz1GHsP7+dsG+IZM20SgpzHSCFDP12tTB3VDLdVDv994nBY3EITZdr+flGKkPvhbaei8YBv95CfFf2sYn5VJXUY2arj4PfMxjLAU3Plc5XJ5/hN5G2TO0kRjzCCAnwP4HwDHADgWwJustafKMX8N4GwAN/U++pK19qKZrrkjpYk7S/yQWAdoKYB7nPj8nz4fPUCClwJdnJfFa+nyfgVd0iHNZjNSbTI5OemSSf72C7oDnVIKGq7zt1bkaFIqnU476oYVMPQ6dU8R3o+TnJ4wgZjnkf5hYlSXjStQ8X8FDCZCi8UiBgYGIgu+6G3zeEY1SuUQeNUj1PUGpJgUqNhf6pn65YlKC/nepYq/eIcgyHZpMlgjCjWEfhSnPwq86lz51/GjTc0X8Hiew3wH6Rmdn37eQp9Pn5/PowCvUZBy+owOfR3WCLFcLm93AtTsaJ25MWYEwEsBXGOtXb9drRBZimDuiyqCTkKfklFvf1cXrWZR5STgEZzJ3+rE4296bUoJ8Dx6T+SNmSAj2OqeMdqnuiGSv+pOQ1pOLF0oRcoom826hVDtdv/lGjQ0XIUKwIEAuWqCP4/TxToK3EqbsE2kc0qlEgYGBiJREvup0+k44GbegEKqi9fk+BBECRQaESjYqJFTAPIjN4Kd0ia8ly6xJ+Ar9aXFBhrJErz0HAV9TYbqeKoX7vPdFLadxsenVSjsZ5+60Wf1nTOep5/zOzUK2t/6Qw+eb8LSfYu63S6GhobwlKc8ZV5z0pcdBvOFlt0BzH3x+Tgf3BXkl4L4lAx3V6SHrKV9quDqiWrIS0+WRkD7hxOL91KPU+u4i8Wio1Y6nf4iKrZVQYFASWAnqPNlFlqrrkv7ydnSs+Qz+JtkabUDn90HKOYOlK6gnvBNVARx5VW5iElLT/X1crqHjY4XdUz7EoB7TgIp265jwWckEPG56aUyKuGbm/RcrZZRLp6G2QdPHueDtW+IgH40otfRCiSON0Wvq9479UV5dToJvnHRH38uaEQSd0+fQqIe0iACwMjIyHZX9M0G5ktqb5ZdWVSBgd1jIRMnEb1oTgh6t/SkW61WhCflxKPnxiQnSxY1tAf6k9Ff7WetdYtnWq2WS4aS8tH3fHa7XceP+5y+7nNOo0CumcCqwEMKhpNRk6n6diaCCkX5YV6P0Q3vU6lUXJKW3DW5c5bYqQGh7jBBrO3WXAL1zV+swvyDesTGmMjr+tQoatSTzWYxODgYqY/39Zreqr5IWftEOW8FcmD6S5k12RmXHFWqRRfMxV1fAZn6wPUNPpVC4FVA9w0Fx5zUmp8UVc6fzoN663yvLt/ytBiSeOaPk8wX3P2Eza4m6m3oZlZAtNJAq0aUt1VQ12oanqeenSYFeW16W/42uQpGXNwBwO3pQq9zamrK7dFSKBTcJAb6iS/lZtWTUyPFdvr8r1IFBGTW+OvGY/riaT4XEC3DoxBIgX7dNYBpBofgSgOhxoZ0FvuGCUJ+r4aXi678xKpiBfVV7+F72LPpuXrXcTqvQKhedlzeSnUuzhjMpsscJ22rOhMcE78qh98rncRz1Zjz/novY4wb922VhGbZBcXn232FWArg3u32q1+opMpfqufIyaur6fwXGmgyizynAplW5jAaIKiTEuHE90snmRykV0qOnuLX45Oa8L1x3SMGiFZLKF2hz6DRAo2Ljq3uacLrKvArlRHHR2tSk20mcChFo966vw2Aln4qgFJoYBUoFQBnizwVaGcCbZ9r95OQ7PO4H58WiRNey59z6ulrglYpK59T177QBVy+oxZn8HiN7Y3IEzBfAuIrdRy466TYlYRJHx/UVcn9kJSAT47WTyoqT+/z2gRF5e8J6gQs9V4rlco06oGgTvDlgiG9n1a2aLjt/6Yh4G81WhwvrfLQ2m6CgdIk/q6RurxfPXjVkTjw0Tpq3RQNiC764fn8TMHKrwDxz4mLKuN0VA2Ezy/rfakffoJzvmCt/ytg+zroe+HaRjWEACIetl5b//YjAp/60euQ8tkeSTjzJSA6EYCltZCJCsrkpG69q+1VxScoKcgw/CcFks/nI3w3PdpKpYJareaoACaXdP8XBQOth2epX6fTiVAwrHApFouRiUqA1D5W4AT6C4FUNERXzpnPy+PZX6waIYjQy1cOm8BPT5LXp3HQ9gH9kjoaAl367yf81NDos/lUho6nH5X4gO0Dt68z86FcaJRmEjVePrWj4l97Jq9bE9d+xKzJV/+Z/WeM65vZ2rcQkoD5Lio+uAO7/kImnZysSqGHyYlCpVZv1Bjjyh25ORc9fCb9uAUvAJdAqlQq7txut+vKwAjy9MbpmepGXcp36oupgf7LqH1QUf5fRY0tFzAx8agrOgE43p4GRF9OoWG6cvS8JxO8BG9+z2fR+/iJOva//7Z4H+T8a8U5DT5F6HPZQFR/lYrQ3ILKbN4/7xn3o/eibsR58XE8exwFFNcOn+ay1k7jxLWNfjSg9+O5iyEJmC8h0TAO2HXBnfx0Op2O1K0TpNhGbbN66rrIiIt2hoaGUC6XMTY25oBeqRH+T2BkxQrQ33KX7SqVSpE6aq5aJTCyb9neuMUmBEl6YzyXG4lp6Z7SJwRtXa2o1TJqLEhRMQegdeUK2myngivvTUrKzz3oPdR4zQTafIaZuGwF7B3RvTgv1gdhv63+2MwEpNvCsato5KLljqo3Ps2kz+PnxRLPPJFpMhO4a7gL7DxwjwN1XVFJekXBTXlK1oSTtslkMthjjz1Qq9UwMTGBTqeDfD7vogBe29r+m5LK5TJKpRKM6S9OUk5YE4ZsAyevcvsEM/Yxj+H3+jwK4OrRAf1X0RHYgf4Oi2yPny/RV7JpG4E+MOh+2yzL1C1gfU9bwUyfKw5A9TztFwWxbZXtAW2frpuN3vDzTAslavR15Sd1yjf8/Nx/9sWQBMx3I/HB3Vd4H9z9pM1itosJTN0+QCcAuV2dIEoz6IrSXC6H0dFRVyMdBOG+LKQhOLm4kyL3P+HLoXWvdPYT0C8LZNRA3prX1JWPbK9W6zAX4Pe7JtWY1GVyUhetKEDqikygv2xcvXDdXyeTyWBgYGDailAFQPa7Gia/DzheOjZxOuJTKrP99oF6vqCtx2qb/fM0onm8HJX5eusznbsYkoD5biw6IYGdv5CJbfFBRVdV6sIZAiA9QoJwrVZzPLgmyIaHhyM13eRPuSdMtVp1L4fmlr4ESPaFghuBmolRtlGB1K+z5rMxqal/s89ZzUBKhsZMaRVr+y+JVhCkoaEB4xuHNAGqxlsBW4XP5b8Ywh97PxEa95s/s9WBU+KiRE1Gav/zednenQXcc8m2euuLJQmYP4FkLnDXxTuLCe5xoK7Ao966Lt/PZDKREshms+l4YNZSZ7NZjIyMRF7mzOtpiZ++M1M9XArBFegDCYGZoKKbgOmr9HzaQl8WzEU5CkpMymrCkvfUBUQ0JsqbE0AI/P540/vfXi57Ni/b/6F+0bD43LRPkdBozcTDqx6qsWUEFBcJzBQdPB6yI976QkgC5k9gmQ3cCXqUuATSjgqV268U4P/KadMTp2fNF0ezWoXVHpVKxVWulMvlyHtAgeh7LunR8s1HWuvOH3riBFiliyYnJ53Xzesqr0zOmp6ZAjOTn9wagefpK+EIfArm2l86jrqUfiGMcByXPRs9MlNi0U8AaqTBcxhBaXJ8pghAP5tvQnEuGmgxjMBs3rrmWhZSEjBPxMlM4K6cJWWhwF2V208Wdrtdt12An3jL5XIYGBiIvDpuYGDAcdKNRsPteBgEQQRw6WWTdmHCkNQL70cPUJOzpHDU+1auXblv9c410UvOnJEESym1IsVffOVTIRq+7wjwxIF2nIdP3fCTqTNdK45qiePFZ2r7tj7TXDSQfvZ4GoE4bz1JgCbyuIufiferCBZyIZMP6kp9KKgriGUyGUepcMHNwMAA8vm8q0snt6xUCaMO8s98QUY+n0epVIK11tEmGqUon8u2sE/U4BCMtV+YcNUSTN1wyU+yzrSb3/ZKnJc9E2jTkM0E2uwDTfb6wB1n7BeT8thej/rxNgKLmZNKwDyReYuG1UD8CrgdLYdUUFcAJfDSiGioyjI8ctZ8Hyd3KGTFiwKwvuOSYK0vW9b2+GCl5YlKpTAC0EodGiaK1nxrAlX3SNmREDzOy/bBh2MzH9DWa85UQhiX0FwqsjOMwGJx5wmYJ7Ld4oM7sHALmQiUrFHXhBfQf+sNPdcgCFxikRtsDQ8PY2BgAJOTk6hWqxFgI6jTOGjNNOkOv7acE5U8uz4Xl+Erb06PvVQquUod9pWu7NzWioc4Hns20PYBe6b7xF0vrhTQX2T0RJQdNQKLIQmYJ7Kg4oeRCoTbA+4zgbrSGcr1M8nIRTnGGAwPD6NYLEYqRnTRDr3vycnJSIUKJ56Cri6hZxShHnaxWHRRhfKlcaH2XB7ajoD2XCDrX28m4N4VSwGXsixmHyZgnsiiShy4KzUTB+5xYERQ1z1XCOLKaWuClMv7lZZheaK11pUvMqFpjHGlhwreBDSlROhl83lYjkceWfdAIfArgOukngmwfdCO46Dn4xnHedtxVSlPdOBeTK85rr8XWhIwT+RxFaUZgNlXqfreO8/PZrORBUeaqFVQJ4AT0I0x7q1HQPhi3YGBAbdSlElK3abW57R1v3M1KqyCUcDms2mFkC44mg20fcCOqxzxvemZaJL5VpTMlBjld4shjxeA7krCCHKhJQHzRHaqKEUCbNtCpjhQ97d85cRhcpJes4J+qVRy2wEQ6NUrJ0dMWkffJRlXrqd18lpBE/fs8wHtuBWclPmUAs5WQhpnTHjuYoh/3YW8z+PV5l3tepR5gbkx5msAng7gSmvtmTMcswrAd6y1z1/A9iXyBJPZwJ0gTSFI6UZZXIDjb+BFcNYFQOTOCfD6wgtdYUlApYFQ6oXX8tukHHkcYMdN6NkmeZy3rfdmnftc90hk95U5wdwY8wYAKWvt4caYc40xT7bW3uMdMwLgmwBKi9TORJ6gMhO4ayUKhcCmAM4SQHLZCrLk4FmnzpdbqHetUQIBlCWHWtmhHvyOAqqflJyrFDAB7kSA+XnmRwG4uPf31QCOBHCPd0wHwFsAXL5gLUskkRhRfhyYXutOwPZ3ZlSum+cBIU3CShcmQvk9AdKvHSf3Tg99RxKGvrcdV1GyVGu4E3l8ZT5gXgLwSO/vCQD7+wdYayeA2cNEY8xaAGsBYPXq1dvazkQSiRW/qkPBnfufcH8WUi2a8GP5It90xJc88wUZ3AKXS/65N8u2gup8a7if6BUliWy/zAfMpwAUen+XAWyXa2CtPR/A+UD4QuftuUYiicwlCu7cuIqgzuSlLuoB4EA7m81iYGAgsgEWE6S5XG7eAJuUAiayM2Q+YH49Qmrl1wAOAnDXorYokUQWWLT2XF/VpkvaW62WW/5PiqVcLrt9XWaSbSkFTIA7kcWU+YD5ZQB+bozZC8AxAI41xpxprT11UVuWSCILLJqo1H1f+JIG7mjIt7P7IB63YnK+NdyJJLLYMieYW2snjDFHAXgpgE9Za9cDuGmGY49ayMYlkshiCCtZdIsAVqkMDg66ksPZdgVMgDuRXU3mVWdurd2KfkVLIonsFuKDur/NLSUpBUxkKUiyAjSRJ7xozTnBPCkFTGSpSQLmiSTSk8V8cUAiiSy2JJqbSCKJJLIbSALmiSSSSCK7gSRgnkgiiSSyG0gC5okkkkgiu4EkYJ5IIokkshuI2Rlv4zDGbATwxx24xHIAmxaoOYkk4kuiX4kspuyIfj3JWrsi7oudAuY7KsaY31lrn7Oz25HI7imJfiWymLJY+pXQLIkkkkgiu4EkYJ5IIokkshvIUgXz83d2AxLZrSXRr0QWUxZFv5YkZ55IIokkkkhUlqpnnkgiiSSSiMiS2WjLGJMF8DRr7c29//cGkLfW3rdzW5bIUhVjTAFA1lo7vrPbksjuLcaYIQCjvZ8VAP4EwMEALrDW/nRB7rFUaBZjTBnAzwC8xFo7Zoz5VwD/ba39/k5uWiJLSIwxfwpgBIAFcAiAwwB8qfd121r7azn2FmvtM4wxywFcZq098nFvcCJLXowxbwLwcQC3A9gI4GEADwC4H8Bd1toFWdOwy4O5MeYjCN9y1AWQA9DofXUogN/2/v5fa+1ZO6F5iSwxMca8CMA+CMH85Qgn1m29r6sAigDeC6AJ4FkAfo8wgv1TADcCyAD4tLX2ssez3YksXTHGHAvgRQAuifk6AHBz7w1uOyRLgWb5ZwBXABjs/e+8J2PMIIDnAPjVTmpbIktP1gM4FUALwDMB3InQQweAS621XwFwAQAYY66x1h5ljFkB4JvW2lfujAYnsuQlh9A5iJMAwIK8tmqXB3Nrbd0YczbC19ZVAfy9MeblAM4BcAaA46y1V+/MNiayNKRn/A8D8G8IPfBVAL4uh6SNMXsi9KDaALrGmJ/K+T8FcKu19m8frzYnslvIMIBfAngHgL297y601v5oIW6yy4N5T9oAjkJo3XIIKZcBhKHyrs0TJbIrSQPA7wAcCODNAI5DqFOn9T6/CsAEgKOstW1jzHMA/BXB2xizL4Cv7IyGJ7KkZTVCMN9TX3pvjDkSwMsW6iZLBcwB4B8BjAP4r53dkESWrBiE+Ze/BtAB8C+9z7MA3oiQbvmktfb2Gc4vIAT7RBLZFnkegI8idEJ9iftsu2QpgfkHEHpWKxC2eym1PZFdQ54EYAtCT/z1AFi5MoAwEXoVwihwmhhjngfgtdix3T4TeYJJL+FesdY2jDGjxpifyNdDAK5cqHstFUAMAPxjryTxub3M7+uNMfvt7IYlsnTEWnsXgLt61QW3AiBXuRzAgdbaC7xTNDH1DIR6+MlFb2giu5M8iNArB4Apa+3R/KJHs7x2oW60VMC8DADGmI8BWNf7uwDgPxAmsxJJZFskDeD/AuDEygJ4NOa4MsJSRFhrv/r4NC2R3Um8RY2v9r77BYBfLNS9dvk680QSSSSRROaWZG+WRBJJJJHdQBIwTySRRBLZDSQB80QSSSSR3UASME8kkUQS2Q0kAfNEEkkkkd1AEjBPJJFEEtkN5P8H1/T08+Mb51YAAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,3))\n",
    "for name, avgs in goods_avgs_dict.items():\n",
    "    if name == \"三星 充电器\":\n",
    "        plt.plot([\"前\",\"春节\",\"后\"], avgs, color='r', label=name)\n",
    "    plt.plot([\"前\",\"春节\",\"后\"], avgs, color='grey',alpha=0.06)\n",
    "plt.plot([\"前\",\"春节\",\"后\"], aavgs,label=\"所有商品平均销售转化率\")\n",
    "plt.legend()\n",
    "plt.title(\"春节前中后三段时间里 各商品的销售转化率\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 试计算 1 月 9 日当天的 ARPU 值\n",
    "\n",
    "答： 1月9日当天的 ARPU=80.07元"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1月9日当天的 ARPU=80.07元\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "Goods = excel_datas['商品信息']\n",
    "\n",
    "sell_01_09 = np.array(Sell[Sell.columns[10]])    # 1月9日的销售情况\n",
    "goods_price = np.array(Goods['单价'])             # 物品单价\n",
    "sell_all = np.dot(sell_01_09, goods_price)  # 1月9日的销售总额 \n",
    "dau_01_09 = get_dau(9)                           # 1月9日的日活跃用户\n",
    "arpu_01_09 = sell_all / dau_01_09\n",
    "print(\"1月9日当天的 ARPU={:.2f}元\".format(arpu_01_09))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 根据此表格提供的信息，以下哪个（些）指标是可被计算出来的？\n",
    "\n",
    "答：\n",
    "\n",
    "1. ARPPU 需要知道付费人数，不可被计算\n",
    "2. 消费人数占比 需要知道付费人数，不可被计算\n",
    "3. 人均下单次数，已知日活跃用户，已知销售情况，故可以计算人均下单次数\n",
    "4. 周留存，除了数据不完整的2月末，皆可以计算周留存"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 使用命令统计access.log的PV和UV\n",
    "\n",
    "![](8_access.log_pv_976226_uv_2467.png)\n",
    "\n",
    "PV = 976226\n",
    "\n",
    "UV = 2467"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 安装goaccess工具，并使用\n",
    "\n",
    "![](9_安装goaccess并使用.png)"
   ]
  }
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